The Frankenstein monster, stitched together from disparate body parts, proved to be an abomination, but stitched together proteins may fare better. They may, for example, serve specific purposes in medicine, research, and industry. At least, that’s the ambition of scientists based at the University of North Carolina. They have developed a computational protocol called SEWING that builds new proteins from connected or disconnected pieces of existing structures. [Wikipedia]

Unlike Victor Frankenstein, who betrayed Promethean ambition when he sewed together his infamous creature, today’s biochemists are relatively modest. Rather than defy nature, they emulate it. For example, at the University of North Carolina (UNC), researchers have taken inspiration from natural evolutionary mechanisms to develop a technique called SEWING—Structure Extension With Native-substructure Graphs. SEWING is a computational protocol that describes how to stitch together new proteins from connected or disconnected pieces of existing structures.

“We can now begin to think about engineering proteins to do things that nothing else is capable of doing,” said UNC’s Brian Kuhlman, Ph.D. “The structure of a protein determines its function, so if we are going to learn how to design new functions, we have to learn how to design new structures. Our study is a critical step in that direction and provides tools for creating proteins that haven’t been seen before in nature.”

Traditionally, researchers have used computational protein design to recreate in the laboratory what already exists in the natural world. In recent years, their focus has shifted toward inventing novel proteins with new functionality. These design projects all start with a specific structural “blueprint” in mind, and as a result are limited. Dr. Kuhlman and his colleagues, however, believe that by removing the limitations of a predetermined blueprint and taking cues from evolution they can more easily create functional proteins.

Dr. Kuhlman’s UNC team developed a protein design approach that emulates natural mechanisms for shuffling tertiary structures such as pleats, coils, and furrows. Putting the approach into action, the UNC team mapped 50,000 stitched together proteins on the computer, and then it produced 21 promising structures in the laboratory. Details of this work appeared May 6 in the journal Science, in an article entitled, “Design of Structurally Distinct Proteins Using Strategies Inspired by Evolution.”

“Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and, in some cases, remain folded at more than 100°C,” wrote the authors. “High-resolution structures of the designed proteins CA01 and DA05R1 were solved by x-ray crystallography (2.2 angstrom resolution) and nuclear magnetic resonance, respectively, and there was excellent agreement with the design models.”

Essentially, the UNC scientists confirmed that the proteins they had synthesized contained the unique structural varieties that had been designed on the computer. The UNC scientists also determined that the structures they had created had new surface and pocket features. Such features, they noted, provide potential binding sites for ligands or macromolecules.

“We were excited that some had clefts or grooves on the surface, regions that naturally occurring proteins use for binding other proteins,” said the Science article’s first author, Tim M. Jacobs, Ph.D., a former graduate student in Dr. Kuhlman’s laboratory. “That’s important because if we wanted to create a protein that can act as a biosensor to detect a certain metabolite in the body, either for diagnostic or research purposes, it would need to have these grooves. Likewise, if we wanted to develop novel therapeutics, they would also need to attach to specific proteins.”

Currently, the UNC researchers are using SEWING to create proteins that can bind to several other proteins at a time. Many of the most important proteins are such multitaskers, including the blood protein hemoglobin.

In some cancers, including chondroblastoma and a rare form of childhood sarcoma, a mutation in histone H3 reduces global levels of methylation (dark areas) in tumor cells but not in normal cells (arrowhead). The mutation locks the cells in a proliferative state to promote tumor development. [Laboratory of Chromatin Biology and Epigenetics at The Rockefeller University]

They have been called oncohistones, the mutated histones that are known to accompany certain pediatric cancers. Despite their suggestive moniker, oncohistones have kept their oncogenic secrets. For example, it has been unclear whether oncohistones are able to cause cancer on their own, or whether they need to act in concert with additional DNA mutations, that is, mutations other than those affecting histone structures.

While oncohistone mechanisms remain poorly understood, this particular question—the oncogenicity of lone oncohistones—has been resolved, at least in part. According to researchers based at The Rockefeller University, a change to the structure of a histone can trigger a tumor on its own.

This finding appeared May 13 in the journal Science, in an article entitled, “Histone H3K36 Mutations Promote Sarcomagenesis Through Altered Histone Methylation Landscape.” The article describes the Rockefeller team’s study of a histone protein called H3, which has been found in about 95% of samples of chondoblastoma, a benign tumor that arises in cartilage, typically during adolescence.

The Rockefeller scientists found that the H3 lysine 36–to–methionine (H3K36M) mutation impairs the differentiation of mesenchymal progenitor cells and generates undifferentiated sarcoma in vivo.

After the scientists inserted the H3 histone mutation into mouse mesenchymal progenitor cells (MPCs)—which generate cartilage, bone, and fat—they watched these cells lose the ability to differentiate in the lab. Next, the scientists injected the mutant cells into living mice, and the animals developed the tumors rich in MPCs, known as an undifferentiated sarcoma. Finally, the researchers tried to understand how the mutation causes the tumors to develop.

The scientists determined that H3K36M mutant nucleosomes inhibit the enzymatic activities of several H3K36 methyltransferases.

“Depleting H3K36 methyltransferases, or expressing an H3K36I mutant that similarly inhibits H3K36 methylation, is sufficient to phenocopy the H3K36M mutation,” the authors of the Science study wrote. “After the loss of H3K36 methylation, a genome-wide gain in H3K27 methylation leads to a redistribution of polycomb repressive complex 1 and de-repression of its target genes known to block mesenchymal differentiation.”

Essentially, when the H3K36M mutation occurs, the cell becomes locked in a proliferative state—meaning it divides constantly, leading to tumors. Specifically, the mutation inhibits enzymes that normally tag the histone with chemical groups known as methyls, allowing genes to be expressed normally.

In response to this lack of modification, another part of the histone becomes overmodified, or tagged with too many methyl groups. “This leads to an overall resetting of the landscape of chromatin, the complex of DNA and its associated factors, including histones,” explained co-author Peter Lewis, Ph.D., a professor at the University of Wisconsin-Madison and a former postdoctoral fellow in laboratory of C. David Allis, Ph.D., a professor at Rockefeller.

The finding—that a “resetting” of the chromatin landscape can lock the cell into a proliferative state—suggests that researchers should be on the hunt for more mutations in histones that might be driving tumors. For their part, the Rockefeller researchers are trying to learn more about how this specific mutation in histone H3 causes tumors to develop.

“We want to know which pathways cause the mesenchymal progenitor cells that carry the mutation to continue to divide, and not differentiate into the bone, fat, and cartilage cells they are destined to become,” said co-author Chao Lu, Ph.D., a postdoctoral fellow in the Allis lab.

Once researchers understand more about these pathways, added Dr. Lewis, they can consider ways of blocking them with drugs, particularly in tumors such as MPC-rich sarcomas—which, unlike chondroblastoma, can be deadly. In fact, drugs that block these pathways may already exist and may even be in use for other types of cancers.

“One long-term goal of our collaborative team is to better understand fundamental mechanisms that drive these processes, with the hope of providing new therapeutic approaches,” concluded Dr. Allis.

Missense mutations (that change one amino acid for another) in histone H3 can produce a so-called oncohistone and are found in a number of pediatric cancers. For example, the lysine-36–to-methionine (K36M) mutation is seen in almost all chondroblastomas. Lu et al. show that K36M mutant histones are oncogenic, and they inhibit the normal methylation of this same residue in wild-type H3 histones. The mutant histones also interfere with the normal development of bone-related cells and the deposition of inhibitory chromatin marks.

The organelle that produces a significant portion of energy for eukaryotic cells would seemingly be indispensable, yet over the years, a number of organisms have been discovered that challenge that biological pretense. However, these so-called amitochondrial species may lack a defined organelle, but they still retain some residual functions of their mitochondria-containing brethren. Even the intestinal eukaryotic parasite Giardia intestinalis, which was for many years considered to be mitochondria-free, was proven recently to contain a considerably shriveled version of the organelle.

Now, an international group of scientists has released results from a new study that challenges the notion that mitochondria are essential for eukaryotes—discovering an organism that resides in the gut of chinchillas that contains absolutely no trace of mitochondria at all.

“In low-oxygen environments, eukaryotes often possess a reduced form of the mitochondrion, but it was believed that some of the mitochondrial functions are so essential that these organelles are indispensable for their life,” explained lead study author Anna Karnkowska, Ph.D., visiting scientist at the University of British Columbia in Vancouver. “We have characterized a eukaryotic microbe which indeed possesses no mitochondrion at all.”

Mysterious Eukaryote Missing Mitochondria

Researchers uncover the first example of a eukaryotic organism that lacks the organelles.

Scientists have long thought that mitochondria—organelles responsible for energy generation—are an essential and defining feature of a eukaryotic cell. Now, researchers from Charles University in Prague and their colleagues are challenging this notion with their discovery of a eukaryotic organism,Monocercomonoides species PA203, which lacks mitochondria. The team’s phylogenetic analysis, published today (May 12) in Current Biology,suggests that Monocercomonoides—which belong to the Oxymonadida group of protozoa and live in low-oxygen environments—did have mitochondria at one point, but eventually lost the organelles.

“This is quite a groundbreaking discovery,” said Thijs Ettema, who studies microbial genome evolution at Uppsala University in Sweden and was not involved in the work.

“This study shows that mitochondria are not so central for all lineages of living eukaryotes,” Toni Gabaldonof the Center for Genomic Regulation in Barcelona, Spain, who also was not involved in the work, wrote in an email to The Scientist. “Yet, this mitochondrial-devoid, single-cell eukaryote is as complex as other eukaryotic cells in almost any other aspect of cellular complexity.”

Charles University’s Vladimir Hampl studies the evolution of protists. Along with Anna Karnkowska and colleagues, Hampl decided to sequence the genome of Monocercomonoides, a little-studied protist that lives in the digestive tracts of vertebrates. The 75-megabase genome—the first of an oxymonad—did not contain any conserved genes found on mitochondrial genomes of other eukaryotes, the researchers found. It also did not contain any nuclear genes associated with mitochondrial functions.

“It was surprising and for a long time, we didn’t believe that the [mitochondria-associated genes were really not there]. We thought we were missing something,” Hampl told The Scientist. “But when the data kept accumulating, we switched to the hypothesis that this organism really didn’t have mitochondria.”

Because researchers have previously not found examples of eukaryotes without some form of mitochondria, the current theory of the origin of eukaryotes poses that the appearance of mitochondria was crucial to the identity of these organisms.

“We now view these mitochondria-like organelles as a continuum from full mitochondria to very small . Some anaerobic protists, for example, have only pared down versions of mitochondria, such as hydrogenosomes and mitosomes, which lack a mitochondrial genome. But these mitochondrion-like organelles perform essential functions of the iron-sulfur cluster assembly pathway, which is known to be conserved in virtually all eukaryotic organisms studied to date.

Yet, in their analysis, the researchers found no evidence of the presence of any components of this mitochondrial pathway.

Like the scaling down of mitochondria into mitosomes in some organisms, the ancestors of modernMonocercomonoides once had mitochondria. “Because this organism is phylogenetically nested among relatives that had conventional mitochondria, this is most likely a secondary adaptation,” said Michael Gray, a biochemist who studies mitochondria at Dalhousie University in Nova Scotia and was not involved in the study. According to Gray, the finding of a mitochondria-deficient eukaryote does not mean that the organelles did not play a major role in the evolution of eukaryotic cells.

To be sure they were not missing mitochondrial proteins, Hampl’s team also searched for potential mitochondrial protein homologs of other anaerobic species, and for signature sequences of a range of known mitochondrial proteins. While similar searches with other species uncovered a few mitochondrial proteins, the team’s analysis of Monocercomonoides came up empty.

“The data is very complete,” said Ettema. “It is difficult to prove the absence of something but [these authors] do a convincing job.”

To form the essential iron-sulfur clusters, the team discovered that Monocercomonoides use a sulfur mobilization system found in the cytosol, and that an ancestor of the organism acquired this system by lateral gene transfer from bacteria. This cytosolic, compensating system allowed Monocercomonoides to lose the otherwise essential iron-sulfur cluster-forming pathway in the mitochondrion, the team proposed.

“This work shows the great evolutionary plasticity of the eukaryotic cell,” said Karnkowska, who participated in the study while she was a postdoc at Charles University. Karnkowska, who is now a visiting researcher at the University of British Columbia in Canada, added: “This is a striking example of how far the evolution of a eukaryotic cell can go that was beyond our expectations.”

“The results highlight how many surprises may await us in the poorly studied eukaryotic phyla that live in under-explored environments,” Gabaldon said.

Ettema agreed. “Now that we’ve found one, we need to look at the bigger picture and see if there are other examples of eukaryotes that have lost their mitochondria, to understand how adaptable eukaryotes are.”

•Monocercomonoides sp. is a eukaryotic microorganism with no mitochondria

•The complete absence of mitochondria is a secondary loss, not an ancestral feature

•The essential mitochondrial ISC pathway was replaced by a bacterial SUF system

The presence of mitochondria and related organelles in every studied eukaryote supports the view that mitochondria are essential cellular components. Here, we report the genome sequence of a microbial eukaryote, the oxymonad Monocercomonoides sp., which revealed that this organism lacks all hallmark mitochondrial proteins. Crucially, the mitochondrial iron-sulfur cluster assembly pathway, thought to be conserved in virtually all eukaryotic cells, has been replaced by a cytosolic sulfur mobilization system (SUF) acquired by lateral gene transfer from bacteria. In the context of eukaryotic phylogeny, our data suggest that Monocercomonoides is not primitively amitochondrial but has lost the mitochondrion secondarily. This is the first example of a eukaryote lacking any form of a mitochondrion, demonstrating that this organelle is not absolutely essential for the viability of a eukaryotic cell.

This method catches a bait protein together with its associated protein partners in virus-like particles that are budded from human cells. Like this, cell lysis is not needed and protein complexes are preserved during purification.

With his feet in both a proteomics lab and an interactomics lab, VIB/UGent professor Sven Eyckerman is well aware of the shortcomings of conventional approaches to analyze protein complexes. The lysis conditions required in mass spectrometry–based strategies to break open cell membranes often affect protein-protein interactions. “The first step in a classical study on protein complexes essentially turns the highly organized cellular structure into a big messy soup”, Eyckerman explains.

Inspired by virus biology, Eyckerman came up with a creative solution. “We used the natural process of HIV particle formation to our benefit by hacking a completely safe form of the virus to abduct intact protein machines from the cell.” It is well known that the HIV virus captures a number of host proteins during its particle formation. By fusing a bait protein to the HIV-1 GAG protein, interaction partners become trapped within virus-like particles that bud from mammalian cells. Standard proteomic approaches are used next to reveal the content of these particles. Fittingly, the team named the method ‘Virotrap’.

The Virotrap approach is exceptional as protein networks can be characterized under natural conditions. By trapping protein complexes in the protective environment of a virus-like shell, the intact complexes are preserved during the purification process. The researchers showed the method was suitable for detection of known binary interactions as well as mass spectrometry-based identification of novel protein partners.

Virotrap is a textbook example of bringing research teams with complementary expertise together. Cross-pollination with the labs of Jan Tavernier (VIB/UGent) and Kris Gevaert (VIB/UGent) enabled the development of this platform.

Jan Tavernier: “Virotrap represents a new concept in co-complex analysis wherein complex stability is physically guaranteed by a protective, physical structure. It is complementary to the arsenal of existing interactomics methods, but also holds potential for other fields, like drug target characterization. We also developed a small molecule-variant of Virotrap that could successfully trap protein partners for small molecule baits.”

Kris Gevaert: “Virotrap can also impact our understanding of disease pathways. We were actually surprised to see that this virus-based system could be used to study antiviral pathways, like Toll-like receptor signaling. Understanding these protein machines in their natural environment is essential if we want to modulate their activity in pathology.“

Cell lysis is an inevitable step in classical mass spectrometry–based strategies to analyse protein complexes. Complementary lysis conditions, in situ cross-linking strategies and proximal labelling techniques are currently used to reduce lysis effects on the protein complex. We have developed Virotrap, a viral particle sorting approach that obviates the need for cell homogenization and preserves the protein complexes during purification. By fusing a bait protein to the HIV-1 GAG protein, we show that interaction partners become trapped within virus-like particles (VLPs) that bud from mammalian cells. Using an efficient VLP enrichment protocol, Virotrap allows the detection of known binary interactions and MS-based identification of novel protein partners as well. In addition, we show the identification of stimulus-dependent interactions and demonstrate trapping of protein partners for small molecules. Virotrap constitutes an elegant complementary approach to the arsenal of methods to study protein complexes.

Proteins mostly exert their function within supramolecular complexes. Strategies for detecting protein–protein interactions (PPIs) can be roughly divided into genetic systems1 and co-purification strategies combined with mass spectrometry (MS) analysis (for example, AP–MS)2. The latter approaches typically require cell or tissue homogenization using detergents, followed by capture of the protein complex using affinity tags3 or specific antibodies4. The protein complexes extracted from this ‘soup’ of constituents are then subjected to several washing steps before actual analysis by trypsin digestion and liquid chromatography–MS/MS analysis. Such lysis and purification protocols are typically empirical and have mostly been optimized using model interactions in single labs. In fact, lysis conditions can profoundly affect the number of both specific and nonspecific proteins that are identified in a typical AP–MS set-up. Indeed, recent studies using the nuclear pore complex as a model protein complex describe optimization of purifications for the different proteins in the complex by examining 96 different conditions5. Nevertheless, for new purifications, it remains hard to correctly estimate the loss of factors in a standard AP–MS experiment due to washing and dilution effects during treatments (that is, false negatives). These considerations have pushed the concept of stabilizing PPIs before the actual homogenization step. A classical approach involves cross-linking with simple reagents (for example, formaldehyde) or with more advanced isotope-labelled cross-linkers (reviewed in ref. 2). However, experimental challenges such as cell permeability and reactivity still preclude the widespread use of cross-linking agents. Moreover, MS-generated spectra of cross-linked peptides are notoriously difficult to identify correctly. A recent lysis-independent solution involves the expression of a bait protein fused to a promiscuous biotin ligase, which results in labelling of proteins proximal to the activity of the enzyme-tagged bait protein6. When compared with AP–MS, this BioID approach delivers a complementary set of candidate proteins, including novel interaction partners7, 8. Such particular studies clearly underscore the need for complementary approaches in the co-complex strategies.

The evolutionary stress on viruses promoted highly condensed coding of information and maximal functionality for small genomes. Accordingly, for HIV-1 it is sufficient to express a single protein, the p55 GAG protein, for efficient production of virus-like particles (VLPs) from cells9, 10. This protein is highly mobile before its accumulation in cholesterol-rich regions of the membrane, where multimerization initiates the budding process11. A total of 4,000–5,000 GAG molecules is required to form a single particle of about 145 nm (ref. 12). Both VLPs and mature viruses contain a number of host proteins that are recruited by binding to viral proteins. These proteins can either contribute to the infectivity (for example, Cyclophilin/FKBPA13) or act as antiviral proteins preventing the spreading of the virus (for example, APOBEC proteins14).

We here describe the development and application of Virotrap, an elegant co-purification strategy based on the trapping of a bait protein together with its associated protein partners in VLPs that are budded from the cell. After enrichment, these particles can be analysed by targeted (for example, western blotting) or unbiased approaches (MS-based proteomics). Virotrap allows detection of known binary PPIs, analysis of protein complexes and their dynamics, and readily detects protein binders for small molecules.

Concept of the Virotrap system

Classical AP–MS approaches rely on cell homogenization to access protein complexes, a step that can vary significantly with the lysis conditions (detergents, salt concentrations, pH conditions and so on)5. To eliminate the homogenization step in AP–MS, we reasoned that incorporation of a protein complex inside a secreted VLP traps the interaction partners under native conditions and protects them during further purification. We thus explored the possibility of protein complex packaging by the expression of GAG-bait protein chimeras (Fig. 1) as expression of GAG results in the release of VLPs from the cells9, 10. As a first PPI pair to evaluate this concept, we selected the HRAS protein as a bait combined with the RAF1 prey protein. We were able to specifically detect the HRAS–RAF1 interaction following enrichment of VLPs via ultracentrifugation (Supplementary Fig. 1a). To prevent tedious ultracentrifugation steps, we designed a novel single-step protocol wherein we co-express the vesicular stomatitis virus glycoprotein (VSV-G) together with a tagged version of this glycoprotein in addition to the GAG bait and prey. Both tagged and untagged VSV-G proteins are probably presented as trimers on the surface of the VLPs, allowing efficient antibody-based recovery from large volumes. The HRAS–RAF1 interaction was confirmed using this single-step protocol (Supplementary Fig. 1b). No associations with unrelated bait or prey proteins were observed for both protocols.

Expression of a GAG-bait fusion protein (1) results in submembrane multimerization (2) and subsequent budding of VLPs from cells (3). Interaction partners of the bait protein are also trapped within these VLPs and can be identified after purification by western blotting or MS analysis (4).

Virotrap for the detection of binary interactions

We next explored the reciprocal detection of a set of PPI pairs, which were selected based on published evidence and cytosolic localization15. After single-step purification and western blot analysis, we could readily detect reciprocal interactions between CDK2 and CKS1B, LCP2 and GRAP2, and S100A1 and S100B (Fig. 2a). Only for the LCP2 prey we observed nonspecific association with an irrelevant bait construct. However, the particle levels of the GRAP2 bait were substantially lower as compared with those of the GAG control construct (GAG protein levels in VLPs; Fig. 2a, second panel of the LCP2 prey). After quantification of the intensities of bait and prey proteins and normalization of prey levels using bait levels, we observed a strong enrichment for the GAG-GRAP2 bait (Supplementary Fig. 2).

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Virotrap for unbiased discovery of novel interactions

For the detection of novel interaction partners, we scaled up VLP production and purification protocols (Supplementary Fig. 5 and Supplementary Note 1 for an overview of the protocol) and investigated protein partners trapped using the following bait proteins: Fas-associated via death domain (FADD), A20 (TNFAIP3), nuclear factor-κB (NF-κB) essential modifier (IKBKG), TRAF family member-associated NF-κB activator (TANK), MYD88 and ring finger protein 41 (RNF41). To obtain specific interactors from the lists of identified proteins, we challenged the data with a combined protein list of 19 unrelated Virotrap experiments (Supplementary Table 1 for an overview). Figure 3 shows the design and the list of candidate interactors obtained after removal of all proteins that were found in the 19 control samples (including removal of proteins from the control list identified with a single peptide). The remaining list of confident protein identifications (identified with at least two peptides in at least two biological repeats) reveals both known and novel candidate interaction partners. All candidate interactors including single peptide protein identifications are given in Supplementary Data 2 and also include recurrent protein identifications of known interactors based on a single peptide; for example, CASP8 for FADD and TANK for NEMO. Using alternative methods, we confirmed the interaction between A20 and FADD, and the associations with transmembrane proteins (insulin receptor and insulin-like growth factor receptor 1) that were captured using RNF41 as a bait (Supplementary Fig. 6). To address the use of Virotrap for the detection of dynamic interactions, we activated the NF-κB pathway via the tumour necrosis factor (TNF) receptor (TNFRSF1A) using TNFα (TNF) and performed Virotrap analysis using A20 as bait (Fig. 3). This resulted in the additional enrichment of receptor-interacting kinase (RIPK1), TNFR1-associated via death domain (TRADD), TNFRSF1A and TNF itself, confirming the expected activated complex20.

Lysis conditions used in AP–MS strategies are critical for the preservation of protein complexes. A multitude of lysis conditions have been described, culminating in a recent report where protein complex stability was assessed under 96 lysis/purification protocols5. Moreover, the authors suggest to optimize the conditions for every complex, implying an important workload for researchers embarking on protein complex analysis using classical AP–MS. As lysis results in a profound change of the subcellular context and significantly alters the concentration of proteins, loss of complex integrity during a classical AP–MS protocol can be expected. A clear evolution towards ‘lysis-independent’ approaches in the co-complex analysis field is evident with the introduction of BioID6 and APEX25 where proximal proteins, including proteins residing in the complex, are labelled with biotin by an enzymatic activity fused to a bait protein. A side-by-side comparison between classical AP–MS and BioID showed overlapping and unique candidate binding proteins for both approaches7, 8, supporting the notion that complementary methods are needed to provide a comprehensive view on protein complexes. This has also been clearly demonstrated for binary approaches15 and is a logical consequence of the heterogenic nature underlying PPIs (binding mechanism, requirement for posttranslational modifications, location, affinity and so on).

In this report, we explore an alternative, yet complementary method to isolate protein complexes without interfering with cellular integrity. By trapping protein complexes in the protective environment of a virus-like shell, the intact complexes are preserved during the purification process. This constitutes a new concept in co-complex analysis wherein complex stability is physically guaranteed by a protective, physical structure. A comparison of our Virotrap approach with AP–MS shows complementary data, with specific false positives and false negatives for both methods (Supplementary Fig. 7).

The current implementation of the Virotrap platform implies the use of a GAG-bait construct resulting in considerable expression of the bait protein. Different strategies are currently pursued to reduce bait expression including co-expression of a native GAG protein together with the GAG-bait protein, not only reducing bait expression but also creating more ‘space’ in the particles potentially accommodating larger bait protein complexes. Nevertheless, the presence of the bait on the forming GAG scaffold creates an intracellular affinity matrix (comparable to the early in vitro affinity columns for purification of interaction partners from lysates26) that has the potential to compete with endogenous complexes by avidity effects. This avidity effect is a powerful mechanism that aids in the recruitment of cyclophilin to GAG27, a well-known weak interaction (Kd=16 μM (ref. 28)) detectable as a background association in the Virotrap system. Although background binding may be increased by elevated bait expression, weaker associations are readily detectable (for example, MAL—MYD88-binding study; Fig. 2c).

The size of Virotrap particles (around 145 nm) suggests limitations in the size of the protein complex that can be accommodated in the particles. Further experimentation is required to define the maximum size of proteins or the number of protein complexes that can be trapped inside the particles.

….

In conclusion, Virotrap captures significant parts of known interactomes and reveals new interactions. This cell lysis-free approach purifies protein complexes under native conditions and thus provides a powerful method to complement AP–MS or other PPI data. Future improvements of the system include strategies to reduce bait expression to more physiological levels and application of advanced data analysis options to filter out background. These developments can further aid in the deployment of Virotrap as a powerful extension of the current co-complex technology arsenal.

New Autism Blood Biomarker Identified

Researchers at UT Southwestern Medical Center have identified a blood biomarker that may aid in earlier diagnosis of children with autism spectrum disorder, or ASD

In a recent edition of Scientific Reports, UT Southwestern researchers reported on the identification of a blood biomarker that could distinguish the majority of ASD study participants versus a control group of similar age range. In addition, the biomarker was significantly correlated with the level of communication impairment, suggesting that the blood test may give insight into ASD severity.

“Numerous investigators have long sought a biomarker for ASD,” said Dr. Dwight German, study senior author and Professor of Psychiatry at UT Southwestern. “The blood biomarker reported here along with others we are testing can represent a useful test with over 80 percent accuracy in identifying ASD.”

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, and restricted, repetitive patterns of behavior. In order to identify individuals with ASD and initiate interventions at the earliest possible age, biomarkers for the disorder are desirable. Research findings have identified widespread changes in the immune system in children with autism, at both systemic and cellular levels. In an attempt to find candidate antibody biomarkers for ASD, highly complex libraries of peptoids (oligo-N-substituted glycines) were screened for compounds that preferentially bind IgG from boys with ASD over typically developing (TD) boys. Unexpectedly, many peptoids were identified that preferentially bound IgG from TD boys. One of these peptoids was studied further and found to bind significantly higher levels (>2-fold) of the IgG1 subtype in serum from TD boys (n = 60) compared to ASD boys (n = 74), as well as compared to older adult males (n = 53). Together these data suggest that ASD boys have reduced levels (>50%) of an IgG1 antibody, which resembles the level found normally with advanced age. In this discovery study, the ASD1 peptoid was 66% accurate in predicting ASD.

….

Peptoid libraries have been used previously to search for autoantibodies for neurodegenerative diseases19 and for systemic lupus erythematosus (SLE)21. In the case of SLE, peptoids were identified that could identify subjects with the disease and related syndromes with moderate sensitivity (70%) and excellent specificity (97.5%). Peptoids were used to measure IgG levels from both healthy subjects and SLE patients. Binding to the SLE-peptoid was significantly higher in SLE patients vs. healthy controls. The IgG bound to the SLE-peptoid was found to react with several autoantigens, suggesting that the peptoids are capable of interacting with multiple, structurally similar molecules. These data indicate that IgG binding to peptoids can identify subjects with high levels of pathogenic autoantibodies vs. a single antibody.

In the present study, the ASD1 peptoid binds significantly lower levels of IgG1 in ASD males vs. TD males. This finding suggests that the ASD1 peptoid recognizes antibody(-ies) of an IgG1 subtype that is (are) significantly lower in abundance in the ASD males vs. TD males. Although a previous study14 has demonstrated lower levels of plasma IgG in ASD vs. TD children, here, we additionally quantified serum IgG levels in our individuals and found no difference in IgG between the two groups (data not shown). Furthermore, our IgG levels did not correlate with ASD1 binding levels, indicating that ASD1 does not bind IgG generically, and that the peptoid’s ability to differentiate between ASD and TD males is related to a specific antibody(-ies).

ASD subjects underwent a diagnostic evaluation using the ADOS and ADI-R, and application of the DSM-IV criteria prior to study inclusion. Only those subjects with a diagnosis of Autistic Disorder were included in the study. The ADOS is a semi-structured observation of a child’s behavior that allows examiners to observe the three core domains of ASD symptoms: reciprocal social interaction, communication, and restricted and repetitive behaviors1. When ADOS subdomain scores were compared with peptoid binding, the only significant relationship was with Social Interaction. However, the positive correlation would suggest that lower peptoid binding is associated with better social interaction, not poorer social interaction as anticipated.

The ADI-R is a structured parental interview that measures the core features of ASD symptoms in the areas of reciprocal social interaction, communication and language, and patterns of behavior. Of the three ADI-R subdomains, only the Communication domain was related to ASD1 peptoid binding, and this correlation was negative suggesting that low peptoid binding is associated with greater communication problems. These latter data are similar to the findings of Heuer et al.14 who found that children with autism with low levels of plasma IgG have high scores on the Aberrant Behavior Checklist (p < 0.0001). Thus, peptoid binding to IgG1 may be useful as a severity marker for ASD allowing for further characterization of individuals, but further research is needed.

It is interesting that in serum samples from older men, the ASD1 binding is similar to that in the ASD boys. This is consistent with the observation that with aging there is a reduction in the strength of the immune system, and the changes are gender-specific25. Recent studies using parabiosis26, in which blood from young mice reverse age-related impairments in cognitive function and synaptic plasticity in old mice, reveal that blood constituents from young subjects may contain important substances for maintaining neuronal functions. Work is in progress to identify the antibody/antibodies that are differentially binding to the ASD1 peptoid, which appear as a single band on the electrophoresis gel (Fig. 4).

……..

The ADI-R is a structured parental interview that measures the core features of ASD symptoms in the areas of reciprocal social interaction, communication and language, and patterns of behavior. Of the three ADI-R subdomains, only the Communication domain was related to ASD1 peptoid binding, and this correlation was negative suggesting that low peptoid binding is associated with greater communication problems. These latter data are similar to the findings of Heuer et al.14 who found that children with autism with low levels of plasma IgG have high scores on the Aberrant Behavior Checklist (p < 0.0001). Thus, peptoid binding to IgG1 may be useful as a severity marker for ASD allowing for further characterization of individuals, but further research is needed.

Titration of IgG binding to ASD1 using serum pooled from 10 TD males and 10 ASD males demonstrates ASD1’s ability to differentiate between the two groups. (B)Detecting IgG1 subclass instead of total IgG amplifies this differentiation. (C) IgG1 binding of individual ASD (n=74) and TD (n=60) male serum samples (1:100 dilution) to ASD1 significantly differs with TD>ASD. In addition, IgG1 binding of older adult male (AM) serum samples (n=53) to ASD1 is significantly lower than TD males, and not different from ASD males. The three groups were compared with a Kruskal-Wallis ANOVA, H = 10.1781, p<0.006. **p<0.005. Error bars show SEM. (D) Receiver-operating characteristic curve for ASD1’s ability to discriminate between ASD and TD males.

Using a computational model they developed, researchers at the University of Pittsburgh School of Medicine have discovered more than 500 new protein-protein interactions (PPIs) associated with genes linked to schizophrenia. The findings, published online in npj Schizophrenia, a Nature Publishing Group journal, could lead to greater understanding of the biological underpinnings of this mental illness, as well as point the way to treatments.

There have been many genome-wide association studies (GWAS) that have identified gene variants associated with an increased risk for schizophrenia, but in most cases there is little known about the proteins that these genes make, what they do and how they interact, said senior investigator Madhavi Ganapathiraju, Ph.D., assistant professor of biomedical informatics, Pitt School of Medicine.

“GWAS studies and other research efforts have shown us what genes might be relevant in schizophrenia,” she said. “What we have done is the next step. We are trying to understand how these genes relate to each other, which could show us the biological pathways that are important in the disease.”

Each gene makes proteins and proteins typically interact with each other in a biological process. Information about interacting partners can shed light on the role of a gene that has not been studied, revealing pathways and biological processes associated with the disease and also its relation to other complex diseases.

Dr. Ganapathiraju’s team developed a computational model called High-Precision Protein Interaction Prediction (HiPPIP) and applied it to discover PPIs of schizophrenia-linked genes identified through GWAS, as well as historically known risk genes. They found 504 never-before known PPIs, and noted also that while schizophrenia-linked genes identified historically and through GWAS had little overlap, the model showed they shared more than 100 common interactors.

“We can infer what the protein might do by checking out the company it keeps,” Dr. Ganapathiraju explained. “For example, if I know you have many friends who play hockey, it could mean that you are involved in hockey, too. Similarly, if we see that an unknown protein interacts with multiple proteins involved in neural signaling, for example, there is a high likelihood that the unknown entity also is involved in the same.”

Dr. Ganapathiraju and colleagues have drawn such inferences on protein function based on the PPIs of proteins, and made their findings available on a website Schizo-Pi. This information can be used by biologists to explore the schizophrenia interactome with the aim of understanding more about the disease or developing new treatment drugs.

(GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein–protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.

Schizophrenia (SZ) is a common, potentially severe psychiatric disorder that afflicts all populations.1 Gene mapping studies suggest that SZ is a complex disorder, with a cumulative impact of variable genetic effects coupled with environmental factors.2 As many as 38 genome-wide association studies (GWAS) have been reported on SZ out of a total of 1,750 GWAS publications on 1,087 traits or diseases reported in the GWAS catalog maintained by the National Human Genome Research Institute of USA3 (as of April 2015), revealing the common variants associated with SZ.4 The SZ Working Group of the Psychiatric Genomics Consortium (PGC) identified 108 genetic loci that likely confer risk for SZ.5 While the role of genetics has been clearly validated by this study, the functional impact of the risk variants is not well-understood.6,7 Several of the genes implicated by the GWAS have unknown functions and could participate in possibly hitherto unknown pathways.8 Further, there is little or no overlap between the genes identified through GWAS and ‘candidate genes’ proposed in the pre-GWAS era.9

Interactome-based studies can be useful in discovering the functional associations of genes. For example,disrupted in schizophrenia 1 (DISC1), an SZ related candidate gene originally had no known homolog in humans. Although it had well-characterized protein domains such as coiled-coil domains and leucine-zipper domains, its function was unknown.10,11 Once its protein–protein interactions (PPIs) were determined using yeast 2-hybrid technology,12 investigators successfully linked DISC1 to cAMP signaling, axon elongation, and neuronal migration, and accelerated the research pertaining to SZ in general, and DISC1 in particular.13 Typically such studies are carried out on known protein–protein interaction (PPI) networks, or as in the case of DISC1, when there is a specific gene of interest, its PPIs are determined by methods such as yeast 2-hybrid technology.

Knowledge of human PPI networks is thus valuable for accelerating discovery of protein function, and indeed, biomedical research in general. However, of the hundreds of thousands of biophysical PPIs thought to exist in the human interactome,14,15 <100,000 are known today (Human Protein Reference Database, HPRD16 and BioGRID17 databases). Gold standard experimental methods for the determination of all the PPIs in human interactome are time-consuming, expensive and may not even be feasible, as about 250 million pairs of proteins would need to be tested overall; high-throughput methods such as yeast 2-hybrid have important limitations for whole interactome determination as they have a low recall of 23% (i.e., remaining 77% of true interactions need to be determined by other means), and a low precision (i.e., the screens have to be repeated multiple times to achieve high selectivity).18,19Computational methods are therefore necessary to complete the interactome expeditiously. Algorithms have begun emerging to predict PPIs using statistical machine learning on the characteristics of the proteins, but these algorithms are employed predominantly to study yeast. Two significant computational predictions have been reported for human interactome; although they have had high false positive rates, these methods have laid the foundation for computational prediction of human PPIs.20,21

We have created a new PPI prediction model called High-Confidence Protein–Protein Interaction Prediction (HiPPIP) model. Novel interactions predicted with this model are making translational impact. For example, we discovered a PPI between OASL and DDX58, which on validation showed that an increased expression of OASL could boost innate immunity to combat influenza by activating the RIG-I pathway.22 Also, the interactome of the genes associated with congenital heart disease showed that the disease morphogenesis has a close connection with the structure and function of cilia.23Here, we describe the HiPPIP model and its application to SZ genes to construct the SZ interactome. After computational evaluations and experimental validations of selected novel PPIs, we present here 504 highly confident novel PPIs in the SZ interactome, shedding new light onto several uncharacterized genes that are associated with SZ.

We developed a computational model called HiPPIP to predict PPIs (see Methods and Supplementary File 1). The model has been evaluated by computational methods and experimental validations and is found to be highly accurate. Evaluations on a held-out test data showed a precision of 97.5% and a recall of 5%. 5% recall out of 150,000 to 600,000 estimated number of interactions in the human interactome corresponds to 7,500–30,000 novel PPIs in the whole interactome. Note that, it is likely that the real precision would be higher than 97.5% because in this test data, randomly paired proteins are treated as non-interacting protein pairs, whereas some of them may actually be interacting pairs with a small probability; thus, some of the pairs that are treated as false positives in test set are likely to be true but hitherto unknown interactions. In Figure 1a, we show the precision versus recall of our method on ‘hub proteins’ where we considered all pairs that received a score >0.5 by HiPPIP to be novel interactions. In Figure 1b, we show the number of true positives versus false positives observed in hub proteins. Both these figures also show our method to be superior in comparison to the prediction of membrane-receptor interactome by Qi et al’s.24 True positives versus false positives are also shown for individual hub proteins by our method in Figure 1cand by Qi et al’s.23 in Figure 1d. These evaluations showed that our predictions contain mostly true positives. Unlike in other domains where ranked lists are commonly used such as information retrieval, in PPI prediction the ‘false positives’ may actually be unlabeled instances that are indeed true interactions that are not yet discovered. In fact, such unlabeled pairs predicted as interactors of the hub gene HMGB1 (namely, the pairs HMGB1-KL and HMGB1-FLT1) were validated by experimental methods and found to be true PPIs (See the Figures e–g inSupplementary File 3). Thus, we concluded that the protein pairs that received a score of ⩾0.5 are highly confident to be true interactions. The pairs that receive a score less than but close to 0.5 (i.e., in the range of 0.4–0.5) may also contain several true PPIs; however, we cannot confidently say that all in this range are true PPIs. Only the PPIs predicted with a score >0.5 are included in the interactome.

Computational evaluation of predicted protein–protein interactions on hub proteins: (a) precision recall curve. (b) True positive versus false positives in ranked lists of hub type membrane receptors for our method and that by Qi et al. True positives versus false positives are shown for individual membrane receptors by our method in (c) and by Qi et al. in (d). Thick line is the average, which is also the same as shown in (b). Note:x-axis is recall in (a), whereas it is number of false positives in (b–d). The range of y-axis is observed by varying the threshold from 1.0–0 in (a), and to 0.5 in (b–d).

SZ interactome

By applying HiPPIP to the GWAS genes and Historic (pre-GWAS) genes, we predicted over 500 high confidence new PPIs adding to about 1400 previously known PPIs.

Schizophrenia interactome: network view of the schizophrenia interactome is shown as a graph, where genes are shown as nodes and PPIs as edges connecting the nodes. Schizophrenia-associated genes are shown as dark blue nodes, novel interactors as red color nodes and known interactors as blue color nodes. The source of the schizophrenia genes is indicated by its label font, where Historic genes are shown italicized, GWAS genes are shown in bold, and the one gene that is common to both is shown in italicized and bold. For clarity, the source is also indicated by the shape of the node (triangular for GWAS and square for Historic and hexagonal for both). Symbols are shown only for the schizophrenia-associated genes; actual interactions may be accessed on the web. Red edges are the novel interactions, whereas blue edges are known interactions. GWAS, genome-wide association studies of schizophrenia; PPI, protein–protein interaction.

We have made the known and novel interactions of all SZ-associated genes available on a webserver called Schizo-Pi, at the addresshttp://severus.dbmi.pitt.edu/schizo-pi. This webserver is similar to Wiki-Pi33 which presents comprehensive annotations of both participating proteins of a PPI side-by-side. The difference between Wiki-Pi which we developed earlier, and Schizo-Pi, is the inclusion of novel predicted interactions of the SZ genes into the latter.

Despite the many advances in biomedical research, identifying the molecular mechanisms underlying the disease is still challenging. Studies based on protein interactions were proven to be valuable in identifying novel gene associations that could shed new light on disease pathology.35 The interactome including more than 500 novel PPIs will help to identify pathways and biological processes associated with the disease and also its relation to other complex diseases. It also helps identify potential drugs that could be repurposed to use for SZ treatment.

Functional and pathway enrichment in SZ interactome

When a gene of interest has little known information, functions of its interacting partners serve as a starting point to hypothesize its own function. We computed statistically significant enrichment of GO biological process terms among the interacting partners of each of the genes using BinGO36 (see online at http://severus.dbmi.pitt.edu/schizo-pi).

Abstract The deposition of proteins in the form of amyloid fibrils and plaques is the characteristic feature of more than 20 degenerative conditions affecting either the central nervous system or a variety of peripheral tissues. As these conditions include Alzheimer’s, Parkinson’s and the prion diseases, several forms of fatal systemic amyloidosis, and at least one condition associated with medical intervention (haemodialysis), they are of enormous importance in the context of present-day human health and welfare. Much remains to be learned about the mechanism by which the proteins associated with these diseases aggregate and form amyloid structures, and how the latter affect the functions of the organs with which they are associated. A great deal of information concerning these diseases has emerged, however, during the past 5 years, much of it causing a number of fundamental assumptions about the amyloid diseases to be reexamined. For example, it is now apparent that the ability to form amyloid structures is not an unusual feature of the small number of proteins associated with these diseases but is instead a general property of polypeptide chains. It has also been found recently that aggregates of proteins not associated with amyloid diseases can impair the ability of cells to function to a similar extent as aggregates of proteins linked with specific neurodegenerative conditions. Moreover, the mature amyloid fibrils or plaques appear to be substantially less toxic than the prefibrillar aggregates that are their precursors. The toxicity of these early aggregates appears to result from an intrinsic ability to impair fundamental cellular processes by interacting with cellular membranes, causing oxidative stress and increases in free Ca2+ that eventually lead to apoptotic or necrotic cell death. The ‘new view’ of these diseases also suggests that other degenerative conditions could have similar underlying origins to those of the amyloidoses. In addition, cellular protection mechanisms, such as molecular chaperones and the protein degradation machinery, appear to be crucial in the prevention of disease in normally functioning living organisms. It also suggests some intriguing new factors that could be of great significance in the evolution of biological molecules and the mechanisms that regulate their behaviour.

The genetic information within a cell encodes not only the specific structures and functions of proteins but also the way these structures are attained through the process known as protein folding. In recent years many of the underlying features of the fundamental mechanism of this complex process and the manner in which it is regulated in living systems have emerged from a combination of experimental and theoretical studies [1]. The knowledge gained from these studies has also raised a host of interesting issues. It has become apparent, for example, that the folding and unfolding of proteins is associated with a whole range of cellular processes from the trafficking of molecules to specific organelles to the regulation of the cell cycle and the immune response. Such observations led to the inevitable conclusion that the failure to fold correctly, or to remain correctly folded, gives rise to many different types of biological malfunctions and hence to many different forms of disease [2]. In addition, it has been recognised recently that a large number of eukaryotic genes code for proteins that appear to be ‘natively unfolded’, and that proteins can adopt, under certain circumstances, highly organised multi-molecular assemblies whose structures are not specifically encoded in the amino acid sequence. Both these observations have raised challenging questions about one of the most fundamental principles of biology: the close relationship between the sequence, structure and function of proteins, as we discuss below [3].

It is well established that proteins that are ‘misfolded’, i.e. that are not in their functionally relevant conformation, are devoid of normal biological activity. In addition, they often aggregate and/or interact inappropriately with other cellular components leading to impairment of cell viability and eventually to cell death. Many diseases, often known as misfolding or conformational diseases, ultimately result from the presence in a living system of protein molecules with structures that are ‘incorrect’, i.e. that differ from those in normally functioning organisms [4]. Such diseases include conditions in which a specific protein, or protein complex, fails to fold correctly (e.g. cystic fibrosis, Marfan syndrome, amyotonic lateral sclerosis) or is not sufficiently stable to perform its normal function (e.g. many forms of cancer). They also include conditions in which aberrant folding behaviour results in the failure of a protein to be correctly trafficked (e.g. familial hypercholesterolaemia, α1-antitrypsin deficiency, and some forms of retinitis pigmentosa) [4]. The tendency of proteins to aggregate, often to give species extremely intractable to dissolution and refolding, is of course also well known in other circumstances. Examples include the formation of inclusion bodies during overexpression of heterologous proteins in bacteria and the precipitation of proteins during laboratory purification procedures. Indeed, protein aggregation is well established as one of the major difficulties associated with the production and handling of proteins in the biotechnology and pharmaceutical industries [5].

Considerable attention is presently focused on a group of protein folding diseases known as amyloidoses. In these diseases specific peptides or proteins fail to fold or to remain correctly folded and then aggregate (often with other components) so as to give rise to ‘amyloid’ deposits in tissue. Amyloid structures can be recognised because they possess a series of specific tinctorial and biophysical characteristics that reflect a common core structure based on the presence of highly organised βsheets [6]. The deposits in strictly defined amyloidoses are extracellular and can often be observed as thread-like fibrillar structures, sometimes assembled further into larger aggregates or plaques. These diseases include a range of sporadic, familial or transmissible degenerative diseases, some of which affect the brain and the central nervous system (e.g. Alzheimer’s and Creutzfeldt-Jakob diseases), while others involve peripheral tissues and organs such as the liver, heart and spleen (e.g. systemic amyloidoses and type II diabetes) [7, 8]. In other forms of amyloidosis, such as primary or secondary systemic amyloidoses, proteinaceous deposits are found in skeletal tissue and joints (e.g. haemodialysis-related amyloidosis) as well as in several organs (e.g. heart and kidney). Yet other components such as collagen, glycosaminoglycans and proteins (e.g. serum amyloid protein) are often present in the deposits protecting them against degradation [9, 10, 11]. Similar deposits to those in the amyloidoses are, however, found intracellularly in other diseases; these can be localised either in the cytoplasm, in the form of specialised aggregates known as aggresomes or as Lewy or Russell bodies or in the nucleus (see below).

The presence in tissue of proteinaceous deposits is a hallmark of all these diseases, suggesting a causative link between aggregate formation and pathological symptoms (often known as the amyloid hypothesis) [7, 8, 12]. At the present time the link between amyloid formation and disease is widely accepted on the basis of a large number of biochemical and genetic studies. The specific nature of the pathogenic species, and the molecular basis of their ability to damage cells, are however, the subject of intense debate [13, 14, 15, 16, 17, 18, 19, 20]. In neurodegenerative disorders it is very likely that the impairment of cellular function follows directly from the interactions of the aggregated proteins with cellular components [21, 22]. In the systemic non-neurological diseases, however, it is widely believed that the accumulation in vital organs of large amounts of amyloid deposits can by itself cause at least some of the clinical symptoms [23]. It is quite possible, however, that there are other more specific effects of aggregates on biochemical processes even in these diseases. The presence of extracellular or intracellular aggregates of a specific polypeptide molecule is a characteristic of all the 20 or so recognised amyloid diseases. The polypeptides involved include full length proteins (e.g. lysozyme or immunoglobulin light chains), biological peptides (amylin, atrial natriuretic factor) and fragments of larger proteins produced as a result of specific processing (e.g. the Alzheimer βpeptide) or of more general degradation [e.g. poly(Q) stretches cleaved from proteins with poly(Q) extensions such as huntingtin, ataxins and the androgen receptor]. The peptides and proteins associated with known amyloid diseases are listed in Table 1. In some cases the proteins involved have wild type sequences, as in sporadic forms of the diseases, but in other cases these are variants resulting from genetic mutations associated with familial forms of the diseases. In some cases both sporadic and familial diseases are associated with a given protein; in this case the mutational variants are usually associated with early-onset forms of the disease. In the case of the neurodegenerative diseases associated with the prion protein some forms of the diseases are transmissible. The existence of familial forms of a number of amyloid diseases has provided significant clues to the origins of the pathologies. For example, there are increasingly strong links between the age at onset of familial forms of disease and the effects of the mutations involved on the propensity of the affected proteins to aggregate in vitro. Such findings also support the link between the process of aggregation and the clinical manifestations of disease [24, 25].

The presence in cells of misfolded or aggregated proteins triggers a complex biological response. In the cytosol, this is referred to as the ‘heat shock response’ and in the endoplasmic reticulum (ER) it is known as the ‘unfolded protein response’. These responses lead to the expression, among others, of the genes for heat shock proteins (Hsp, or molecular chaperone proteins) and proteins involved in the ubiquitin-proteasome pathway [26]. The evolution of such complex biochemical machinery testifies to the fact that it is necessary for cells to isolate and clear rapidly and efficiently any unfolded or incorrectly folded protein as soon as it appears. In itself this fact suggests that these species could have a generally adverse effect on cellular components and cell viability. Indeed, it was a major step forward in understanding many aspects of cell biology when it was recognised that proteins previously associated only with stress, such as heat shock, are in fact crucial in the normal functioning of living systems. This advance, for example, led to the discovery of the role of molecular chaperones in protein folding and in the normal ‘housekeeping’ processes that are inherent in healthy cells [27, 28]. More recently a number of degenerative diseases, both neurological and systemic, have been linked to, or shown to be affected by, impairment of the ubiquitin-proteasome pathway (Table 2). The diseases are primarily associated with a reduction in either the expression or the biological activity of Hsps, ubiquitin, ubiquitinating or deubiquitinating enzymes and the proteasome itself, as we show below [29, 30, 31, 32], or even to the failure of the quality control mechanisms that ensure proper maturation of proteins in the ER. The latter normally leads to degradation of a significant proportion of polypeptide chains before they have attained their native conformations through retrograde translocation to the cytosol [33, 34].

….

It is now well established that the molecular basis of protein aggregation into amyloid structures involves the existence of ‘misfolded’ forms of proteins, i.e. proteins that are not in the structures in which they normally function in vivo or of fragments of proteins resulting from degradation processes that are inherently unable to fold [4, 7, 8, 36]. Aggregation is one of the common consequences of a polypeptide chain failing to reach or maintain its functional three-dimensional structure. Such events can be associated with specific mutations, misprocessing phenomena, aberrant interactions with metal ions, changes in environmental conditions, such as pH or temperature, or chemical modification (oxidation, proteolysis). Perturbations in the conformational properties of the polypeptide chain resulting from such phenomena may affect equilibrium 1 in Fig. 1 increasing the population of partially unfolded, or misfolded, species that are much more aggregation-prone than the native state.

Fig. 1 Overview of the possible fates of a newly synthesised polypeptide chain. The equilibrium ① between the partially folded molecules and the natively folded ones is usually strongly in favour of the latter except as a result of specific mutations, chemical modifications or partially destabilising solution conditions. The increased equilibrium populations of molecules in the partially or completely unfolded ensemble of structures are usually degraded by the proteasome; when this clearance mechanism is impaired, such species often form disordered aggregates or shift equilibrium ② towards the nucleation of pre-fibrillar assemblies that eventually grow into mature fibrils (equilibrium ③). DANGER! indicates that pre-fibrillar aggregates in most cases display much higher toxicity than mature fibrils. Heat shock proteins (Hsp) can suppress the appearance of pre-fibrillar assemblies by minimising the population of the partially folded molecules by assisting in the correct folding of the nascent chain and the unfolded protein response target incorrectly folded proteins for degradation.

……

Little is known at present about the detailed arrangement of the polypeptide chains themselves within amyloid fibrils, either those parts involved in the core βstrands or in regions that connect the various β-strands. Recent data suggest that the sheets are relatively untwisted and may in some cases at least exist in quite specific supersecondary structure motifs such as β-helices [6, 40] or the recently proposed µ-helix [41]. It seems possible that there may be significant differences in the way the strands are assembled depending on characteristics of the polypeptide chain involved [6, 42]. Factors including length, sequence (and in some cases the presence of disulphide bonds or post-translational modifications such as glycosylation) may be important in determining details of the structures. Several recent papers report structural models for amyloid fibrils containing different polypeptide chains, including the Aβ40 peptide, insulin and fragments of the prion protein, based on data from such techniques as cryo-electron microscopy and solid-state magnetic resonance spectroscopy [43, 44]. These models have much in common and do indeed appear to reflect the fact that the structures of different fibrils are likely to be variations on a common theme [40]. It is also emerging that there may be some common and highly organised assemblies of amyloid protofilaments that are not simply extended threads or ribbons. It is clear, for example, that in some cases large closed loops can be formed [45, 46, 47], and there may be specific types of relatively small spherical or ‘doughnut’ shaped structures that can result in at least some circumstances (see below).

…..

The similarity of some early amyloid aggregates with the pores resulting from oligomerisation of bacterial toxins and pore-forming eukaryotic proteins (see below) also suggest that the basic mechanism of protein aggregation into amyloid structures may not only be associated with diseases but in some cases could result in species with functional significance. Recent evidence indicates that a variety of micro-organisms may exploit the controlled aggregation of specific proteins (or their precursors) to generate functional structures. Examples include bacterial curli [52] and proteins of the interior fibre cells of mammalian ocular lenses, whose β-sheet arrays seem to be organised in an amyloid-like supramolecular order [53]. In this case the inherent stability of amyloid-like protein structure may contribute to the long-term structural integrity and transparency of the lens. Recently it has been hypothesised that amyloid-like aggregates of serum amyloid A found in secondary amyloidoses following chronic inflammatory diseases protect the host against bacterial infections by inducing lysis of bacterial cells [54]. One particularly interesting example is a ‘misfolded’ form of the milk protein α-lactalbumin that is formed at low pH and trapped by the presence of specific lipid molecules [55]. This form of the protein has been reported to trigger apoptosis selectively in tumour cells providing evidence for its importance in protecting infants from certain types of cancer [55]. ….

Amyloid formation is a generic property of polypeptide chains ….

It is clear that the presence of different side chains can influence the details of amyloid structures, particularly the assembly of protofibrils, and that they give rise to the variations on the common structural theme discussed above. More fundamentally, the composition and sequence of a peptide or protein affects profoundly its propensity to form amyloid structures under given conditions (see below).

Because the formation of stable protein aggregates of amyloid type does not normally occur in vivo under physiological conditions, it is likely that the proteins encoded in the genomes of living organisms are endowed with structural adaptations that mitigate against aggregation under these conditions. A recent survey involving a large number of structures of β-proteins highlights several strategies through which natural proteins avoid intermolecular association of β-strands in their native states [65]. Other surveys of protein databases indicate that nature disfavours sequences of alternating polar and nonpolar residues, as well as clusters of several consecutive hydrophobic residues, both of which enhance the tendency of a protein to aggregate prior to becoming completely folded [66, 67].

……

Precursors of amyloid fibrils can be toxic to cells

It was generally assumed until recently that the proteinaceous aggregates most toxic to cells are likely to be mature amyloid fibrils, the form of aggregates that have been commonly detected in pathological deposits. It therefore appeared probable that the pathogenic features underlying amyloid diseases are a consequence of the interaction with cells of extracellular deposits of aggregated material. As well as forming the basis for understanding the fundamental causes of these diseases, this scenario stimulated the exploration of therapeutic approaches to amyloidoses that focused mainly on the search for molecules able to impair the growth and deposition of fibrillar forms of aggregated proteins. ….

Structural basis and molecular features of amyloid toxicity

The presence of toxic aggregates inside or outside cells can impair a number of cell functions that ultimately lead to cell death by an apoptotic mechanism [95, 96]. Recent research suggests, however, that in most cases initial perturbations to fundamental cellular processes underlie the impairment of cell function induced by aggregates of disease-associated polypeptides. Many pieces of data point to a central role of modifications to the intracellular redox status and free Ca2+ levels in cells exposed to toxic aggregates [45, 89, 97, 98, 99, 100, 101]. A modification of the intracellular redox status in such cells is associated with a sharp increase in the quantity of reactive oxygen species (ROS) that is reminiscent of the oxidative burst by which leukocytes destroy invading foreign cells after phagocytosis. In addition, changes have been observed in reactive nitrogen species, lipid peroxidation, deregulation of NO metabolism [97], protein nitrosylation [102] and upregulation of heme oxygenase-1, a specific marker of oxidative stress [103]. ….

Results have recently been reported concerning the toxicity towards cultured cells of aggregates of poly(Q) peptides which argues against a disease mechanism based on specific toxic features of the aggregates. These results indicate that there is a close relationship between the toxicity of proteins with poly(Q) extensions and their nuclear localisation. In addition they support the hypotheses that the toxicity of poly(Q) aggregates can be a consequence of altered interactions with nuclear coactivator or corepressor molecules including p53, CBP, Sp1 and TAF130 or of the interaction with transcription factors and nuclear coactivators, such as CBP, endowed with short poly(Q) stretches ([95] and references therein)…..

Concluding remarks
The data reported in the past few years strongly suggest that the conversion of normally soluble proteins into amyloid fibrils and the toxicity of small aggregates appearing during the early stages of the formation of the latter are common or generic features of polypeptide chains. Moreover, the molecular basis of this toxicity also appears to display common features between the different systems that have so far been studied. The ability of many, perhaps all, natural polypeptides to ‘misfold’ and convert into toxic aggregates under suitable conditions suggests that one of the most important driving forces in the evolution of proteins must have been the negative selection against sequence changes that increase the tendency of a polypeptide chain to aggregate. Nevertheless, as protein folding is a stochastic process, and no such process can be completely infallible, misfolded proteins or protein folding intermediates in equilibrium with the natively folded molecules must continuously form within cells. Thus mechanisms to deal with such species must have co-evolved with proteins. Indeed, it is clear that misfolding, and the associated tendency to aggregate, is kept under control by molecular chaperones, which render the resulting species harmless assisting in their refolding, or triggering their degradation by the cellular clearance machinery [166, 167, 168, 169, 170, 171, 172, 173, 175, 177, 178].

Misfolded and aggregated species are likely to owe their toxicity to the exposure on their surfaces of regions of proteins that are buried in the interior of the structures of the correctly folded native states. The exposure of large patches of hydrophobic groups is likely to be particularly significant as such patches favour the interaction of the misfolded species with cell membranes [44, 83, 89, 90, 91, 93]. Interactions of this type are likely to lead to the impairment of the function and integrity of the membranes involved, giving rise to a loss of regulation of the intracellular ion balance and redox status and eventually to cell death. In addition, misfolded proteins undoubtedly interact inappropriately with other cellular components, potentially giving rise to the impairment of a range of other biological processes. Under some conditions the intracellular content of aggregated species may increase directly, due to an enhanced propensity of incompletely folded or misfolded species to aggregate within the cell itself. This could occur as the result of the expression of mutational variants of proteins with decreased stability or cooperativity or with an intrinsically higher propensity to aggregate. It could also occur as a result of the overproduction of some types of protein, for example, because of other genetic factors or other disease conditions, or because of perturbations to the cellular environment that generate conditions favouring aggregation, such as heat shock or oxidative stress. Finally, the accumulation of misfolded or aggregated proteins could arise from the chaperone and clearance mechanisms becoming overwhelmed as a result of specific mutant phenotypes or of the general effects of ageing [173, 174].

The topics discussed in this review not only provide a great deal of evidence for the ‘new view’ that proteins have an intrinsic capability of misfolding and forming structures such as amyloid fibrils but also suggest that the role of molecular chaperones is even more important than was thought in the past. The role of these ubiquitous proteins in enhancing the efficiency of protein folding is well established [185]. It could well be that they are at least as important in controlling the harmful effects of misfolded or aggregated proteins as in enhancing the yield of functional molecules.

Nutritional Status is Associated with Faster Cognitive Decline and Worse Functional Impairment in the Progression of Dementia: The Cache County Dementia Progression Study1

Nutritional status may be a modifiable factor in the progression of dementia. We examined the association of nutritional status and rate of cognitive and functional decline in a U.S. population-based sample. Study design was an observational longitudinal study with annual follow-ups up to 6 years of 292 persons with dementia (72% Alzheimer’s disease, 56% female) in Cache County, UT using the Mini-Mental State Exam (MMSE), Clinical Dementia Rating Sum of Boxes (CDR-sb), and modified Mini Nutritional Assessment (mMNA). mMNA scores declined by approximately 0.50 points/year, suggesting increasing risk for malnutrition. Lower mMNA score predicted faster rate of decline on the MMSE at earlier follow-up times, but slower decline at later follow-up times, whereas higher mMNA scores had the opposite pattern (mMNA by time β= 0.22, p = 0.017; mMNA by time2 β= –0.04, p = 0.04). Lower mMNA score was associated with greater impairment on the CDR-sb over the course of dementia (β= 0.35, p < 0.001). Assessment of malnutrition may be useful in predicting rates of progression in dementia and may provide a target for clinical intervention.

Background: Considerable evidence has been reported for the comorbidity between late-life depression (LLD) and Alzheimer’s disease (AD), both of which are very common in the general elderly population and represent a large burden on the health of the elderly. The pathophysiological mechanisms underlying the link between LLD and AD are poorly understood. Because both LLD and AD can be heritable and are influenced by multiple risk genes, shared genetic risk factors between LLD and AD may exist. Objective: The objective is to review the existing evidence for genetic risk factors that are common to LLD and AD and to outline the biological substrates proposed to mediate this association. Methods: A literature review was performed. Results: Genetic polymorphisms of brain-derived neurotrophic factor, apolipoprotein E, interleukin 1-beta, and methylenetetrahydrofolate reductase have been demonstrated to confer increased risk to both LLD and AD by studies examining either LLD or AD patients. These results contribute to the understanding of pathophysiological mechanisms that are common to both of these disorders, including deficits in nerve growth factors, inflammatory changes, and dysregulation mechanisms involving lipoprotein and folate. Other conflicting results have also been reviewed, and few studies have investigated the effects of the described polymorphisms on both LLD and AD. Conclusion: The findings suggest that common genetic pathways may underlie LLD and AD comorbidity. Studies to evaluate the genetic relationship between LLD and AD may provide insights into the molecular mechanisms that trigger disease progression as the population ages.

Objective To investigate the association of circulating levels of vitamin B12, red blood cell folate, and sulfur amino acids with the rate of total brain volume loss and the change in white matter hyperintensity volume as measured by fluid-attenuated inversion recovery in older adults.

Design, Setting, and Participants The magnetic resonance imaging subsample of the Swedish National Study on Aging and Care in Kungsholmen, a population-based longitudinal study in Stockholm, Sweden, was conducted in 501 participants aged 60 years or older who were free of dementia at baseline. A total of 299 participants underwent repeated structural brain magnetic resonance imaging scans from September 17, 2001, to December 17, 2009.

Main Outcomes and Measures The rate of brain tissue volume loss and the progression of total white matter hyperintensity volume.

Conclusions and Relevance This study suggests that both vitamin B12 and total homocysteine concentrations may be related to accelerated aging of the brain. Randomized clinical trials are needed to determine the importance of vitamin B12supplementation on slowing brain aging in older adults.

Notes from Kurzweill

This vitamin stops the aging process in organs, say Swiss researchers

A potential breakthrough for regenerative medicine, pending further studies

EPFL researchers have restored the ability of mice organs to regenerate and extend life by simply administering nicotinamide riboside (NR) to them.

NR has been shown in previous studies to be effective in boosting metabolism and treating a number of degenerative diseases. Now, an article by PhD student Hongbo Zhang published in Science also describes the restorative effects of NR on the functioning of stem cells for regenerating organs.

As in all mammals, as mice age, the regenerative capacity of certain organs (such as the liver and kidneys) and muscles (including the heart) diminishes. Their ability to repair them following an injury is also affected. This leads to many of the disorders typical of aging.

Mitochondria —> stem cells —> organs

To understand how the regeneration process deteriorates with age, Zhang teamed up with colleagues from ETH Zurich, the University of Zurich, and universities in Canada and Brazil. By using several biomarkers, they were able to identify the molecular chain that regulates how mitochondria — the “powerhouse” of the cell — function and how they change with age. “We were able to show for the first time that their ability to function properly was important for stem cells,” said Auwerx.

Under normal conditions, these stem cells, reacting to signals sent by the body, regenerate damaged organs by producing new specific cells. At least in young bodies. “We demonstrated that fatigue in stem cells was one of the main causes of poor regeneration or even degeneration in certain tissues or organs,” said Zhang.

How to revitalize stem cells

Which is why the researchers wanted to “revitalize” stem cells in the muscles of elderly mice. And they did so by precisely targeting the molecules that help the mitochondria to function properly. “We gave nicotinamide riboside to 2-year-old mice, which is an advanced age for them,” said Zhang.

“This substance, which is close to vitamin B3, is a precursor of NAD+, a molecule that plays a key role in mitochondrial activity. And our results are extremely promising: muscular regeneration is much better in mice that received NR, and they lived longer than the mice that didn’t get it.”

Parallel studies have revealed a comparable effect on stem cells of the brain and skin. “This work could have very important implications in the field of regenerative medicine,” said Auwerx. This work on the aging process also has potential for treating diseases that can affect — and be fatal — in young people, like muscular dystrophy (myopathy).

So far, no negative side effects have been observed following the use of NR, even at high doses. But while it appears to boost the functioning of all cells, it could include pathological ones, so further in-depth studies are required.

Abstract of NAD+ repletion improves mitochondrial and stem cell function and enhances life span in mice

Adult stem cells (SCs) are essential for tissue maintenance and regeneration yet are susceptible to senescence during aging. We demonstrate the importance of the amount of the oxidized form of cellular nicotinamide adenine dinucleotide (NAD+) and its impact on mitochondrial activity as a pivotal switch to modulate muscle SC (MuSC) senescence. Treatment with the NAD+ precursor nicotinamide riboside (NR) induced the mitochondrial unfolded protein response (UPRmt) and synthesis of prohibitin proteins, and this rejuvenated MuSCs in aged mice. NR also prevented MuSC senescence in the Mdx mouse model of muscular dystrophy. We furthermore demonstrate that NR delays senescence of neural SCs (NSCs) and melanocyte SCs (McSCs), and increased mouse lifespan. Strategies that conserve cellular NAD+ may reprogram dysfunctional SCs and improve lifespan in mammals.

Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer–promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer–promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.

On observing schizophrenia from a clinical point of view up to its molecular basis, one may conclude that this is likely to be one of the most complex human disorders to be characterized in all aspects. Such complexity is the reflex of an intricate combination of genetic and environmental components that influence brain functions since pre-natal neurodevelopment, passing by brain maturation, up to the onset of disease and disease establishment. The perfect function of tissues, organs, systems, and finally the organism depends heavily on the proper functioning of cells. Several lines of evidence, including genetics, genomics, transcriptomics, neuropathology, and pharmacology, have supported the idea that dysfunctional cells are causative to schizophrenia. Together with the above-mentioned techniques, proteomics have been contributing to understanding the biochemical basis of schizophrenia at the cellular and tissue level through the identification of differentially expressed proteins and consequently their biochemical pathways, mostly in the brain tissue but also in other cells. In addition, mass spectrometry-based proteomics have identified and precisely quantified proteins that may serve as biomarker candidates to prognosis, diagnosis, and medication monitoring in peripheral tissue. Here, we review all data produced by proteomic investigation in the last 5 years using tissue and/or cells from schizophrenic patients, focusing on postmortem brain tissue and peripheral blood serum and plasma. This information has provided integrated pictures of the biochemical systems involved in the pathobiology, and has suggested potential biomarkers, and warrant potential targets to alternative treatment therapies to schizophrenia.

Schizophrenia is a complex neuropsychiatric disorder that produces severe symptoms and significant lifelong disability, causing massive personal and societal burden.1,2 About 1% of the world’s population is affected by schizophrenia.3 Despite the strong genetic component, showing increasing risks for those related to schizophrenic patients,4 and the known role of environment as a trigger, schizophrenia signs and symptoms have unknown etiology. Currently, the disease diagnosis is essentially clinically defined by observed signs of psychosis, which often include paranoid delusions and auditory hallucinations,5 with onset during late adolescence and/or early adulthood.

Pharmacological treatments are available for schizophrenia; yet, most of the currently used antipsychotic medications were discovered in the 1950s, or are a variation of those medications, and since then no new major drug class has been introduced to the clinic. In addition, efficacy of medication is poor, and only about 40% of schizophrenic patients respond effectively to initial treatment with antipsychotics.6,7 Unfortunately, comprehensive studies on molecular mechanisms of schizophrenia have been scant; hence, current treatments are only partly beneficial to a subset of symptoms. The response to drugs is heterogenous, mainly because of individual variations of the disease, in addition to scarce knowledge on its pathophysiology, impairing both diagnosis and adequate treatment selection.8,9

Heterogenic and multifactorial aspects of schizophrenia have always hindered biochemical characterization studies and delayed the establishment of preclinical models of the disease.10 Several studies, including postmortem, imaging, pharmacological, and genetic studies, reported common traces of the disease, such as synaptic deficits, abnormal neural network, and changes in neurotransmission, involving dopamine, glutamate, and gamma-aminobutyric acid.2,11,12,13 Additional abnormalities, such as aberrant inflammatory responses, oligodendrocyte alterations, epigenetic changes, mitochondrial dysfunction, and reactive oxygen species (ROS) imbalance, are often described in schizophrenia.14,15,16

A complex cross talk between genetic and environmental factors during neurogenesis is responsible for promoting differences of gene and protein expression in schizophrenia, causing abnormal processes during neurodevelopment.2 Recent studies found reinforcement of genes associated with the major hypotheses of glutamatergic neurotransmission, such as DRD2 (dopamine receptor D2)—the main target of antipsychotic drugs17—among other potential targets, involving perturbation of specific neurotransmitter systems or pathways, which are yet to be studied. The complexity of schizophrenia reinforces the need to unravel molecular mechanisms, as those insights have been shown to be essential in identifying and validating drug targets and biomarkers.9 Therefore, unraveling models with relevance to the cause and onset of schizophrenia is essential toward improving treatments and outcomes for those with the disorder.

Here we review the advances of proteomics on schizophrenia research, toward a better understanding of disease mechanisms and response to treatment, and the efforts toward the discovery of biomarkers for diagnosis and disease evolution.

The role of proteomics in schizophrenia research

In the past century, psychiatric research was dedicated to understanding the nature of several disorders, including action of psychotherapeutics. It was also shown that schizophrenia is a highly heritable disease, indicating a strong genetic influence and an estimated heritability of 80–85%,18,19 more likely with a polygenic basis.20 Since the beginning of the twenty-first century, revolution of genomic technologies has allowed a deeper understanding of the genetic basis of diseases, and several genetic findings on psychiatric disorders have been reported,21 unraveling candidate genes linked to risk factors of psychiatric disorders, such as DISC1 (disrupted in schizophrenia 1),22 involved in neuronal development and synapse formation.23,24 In fact, the International Schizophrenia Consortium (ISC) found indication for a polygenic contribution to schizophrenia.25 While candidate gene studies are beneficial, in cases with a not yet well-understood biology, such as schizophrenia, a single gene only adds a small phenotype effect to the multifactorial etiology of the disease.20,26,27

Since 2008, genomic technology innovations have led to a better understanding of psychiatric disorders, providing information about numerous genes that have a role in brain development.21 Recent advances of next-generation sequencing have facilitated a higher coverage and sample throughput of schizophrenia studies.28,29,30Furthermore, international collaborations, which increased the number of participant subjects and samples, have combined efforts to provide deeper insight from comprehensive biological data sets, such as the Psychiatric GWAS (genome-wide association studies) Consortium (PGC; http://pgc.unc.edu).31 Most recently, two main studies, reporting comprehensive GWAS analysis, were able to identify 13 (ref. 27) and 108 schizophrenia-associated risk loci,17 the latter being the largest GWAS study on schizophrenia to date, with up to 36,989 cases and 113,075 controls. Unbiased GWAS,17,27,32,33 indicating genetic regions (loci) that contribute to disease susceptibility, and structural variation studies, such as copy number variants,30,34 are the main identification sources of gene variants with small effects on disease phenotype.35 For instance, copy number variants, including deletions and duplications of several DNA segments, confer significant risk increase in alleles of schizophrenia genome up to 10–25-fold.9,34,36Several of those findings support the leading etiological hypothesis of the disorder, and point to functionally related targets, such as DRD2, miRNA-137, N-methyl-D-aspartate receptor (NMDAR) complex, or calcium channel subunits.17,30,36,37 Information on genetic variations as a base will increase knowledge on mechanisms of schizophrenia and other psychiatric disorders.

Deciphering the human genome was a revolution in genetics, and the anticipated next step was to decode RNA complexity to understand how information was delivered, and its variety between individuals. Development of large-scale transcriptome analyses, such as cDNA microarrays, Serial Analysis of Gene Expression, and the analyses of Expressed Sequence Tag, and more recently the advance of whole transcriptome shotgun sequencing (or RNA-Seq), providing the presence and quantification of RNA at a given time in a genome, allowed a deeper insight into the dynamics of an organism. Transcriptome analyses revealed RNA implication in psychiatric diseases,38,39 including abnormalities resulting from alternative splicing, in addition to messenger RNA transcripts, such as total RNA and small RNA, including micro-RNA.40 Those abnormalities were observed in several biological processes, such as synaptic and mitochondrial/energetic function,41,42,43 cytoskeleton,44 immune and inflammation response,45,46,47 and the myelination pathway.48 Although not yet fully understood, the more the pieces of the puzzle discovered, the more comprehensive the pathology network becomes.

Genomic and transcriptomic studies generated significant data, although these changes cannot yet be translated into biomarkers. The main limitation of genetic approaches in schizophrenia is extrapolation to functional protein expression, as proteins undergo several modifications from transcription to posttranslation, and transcript abundance cannot really predict protein levels either in normal conditions or in response to stress, such as diseases.49,50 Therefore, proteomic techniques are being increasingly used in screening for identification of biomarkers in schizophrenia,51,52 providing several insights into the pathophysiology of the disease. Proteomics can show global expression of proteins or protein groups, and is more complex than genomics as it can change from each cell type at any given time or state.49 Also a high-throughput method, proteomic studies detect fewer expressed proteins than a transcriptomic detects expressed genes, but protein expression provides a precise functional profile and presents an unbiased current physiological state as a reflex of the complex interaction of gene versus environment. The importance of those interactions has been increasing in the research of schizophrenia and other neurological diseases.10,41,53

Regarding research into schizophrenia, numerous studies have investigated the proteome of postmortem brain tissue, including several brain regions such as the dorsolateral prefrontal cortex,41,54,55 frontal cortex,56thalamus,57 anterior cingulate cortex,58,59,60hippocampus,61,62 corpus callosum,63 and insular cortex.64 Postmortem brain tissue has yielded many valuable insights into the pathophysiology of schizophrenia, but less information on disease onset and development. Thus, other tissues and cells have been tested, providing data from naive patients as well, such as from cerebrospinal fluid (CSF),65,66,67,68blood serum and plasma,69,70,71,72,73 liver,68,74 and fibroblasts,75 which can be biopsied from living patients, among others,76,77aiming to reveal more about potential biomarkers of discovery and monitoring of the disease.

Proteomic methodologies used in schizophrenia research

A proteome comprises the entire set of proteins in a biological system (cell, tissue, or organism) in a particular state, at a given time.78 The need to understand all proteins derived from almost 20,000 genes identified by the Human Genome Project turns molecular biology studies toward proteomics. Because of the progress of mass spectrometry techniques, more fine and high-throughput methods are available, supporting the identification of hundreds (or thousands) of proteins in a single biological sample. In 2014, two major consortiums have delivered a draft of the human proteome,79,80 with a large-scale data set covering 84–92% of the protein-coded genes annotated for the human genome. The more information annotated on protein knowledge databases, the more unknown causes of diseases and biomarker identification can be performed.

In the first decade of proteomics, the main quantitative methods used were gel-based, such as two-dimensional gel electrophoresis (2DE), including the fluorescent two-dimensional differential gel electrophoresis (2D-DIGE). Despite its recognized usefulness,81 gel-based techniques have been consistently replaced by gel-free techniques with the introduction of the concept of shotgun proteomics, which employs basically liquid chromatography followed by mass spectrometry (LC/MS).82 The large scale was possible only because of the development of proteomics based on mass spectrometry, which offers insights into protein abundance, expression profiles according to cell type, posttranslational modifications, and protein–protein interactions, and the possibility to study modifications at the protein level.83

2DE was first described in 1975,84 and after intense enhancements in the 1980s85,86 it became widely used in the separation of complex protein mixtures according to their isoelectric point, in the first dimension by isoelectric focusing, and according to their molecular weight (MW), in the second dimension, by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. This separation leads to a protein profile comprising several spots, each of which, in theory, represents a single protein, providing information about intact proteins and isoforms. Protein visualization techniques include common post-run methods, such as Coomassie blue or silver staining, and also pre-labeling of samples with fluorescent dies, such as in the 2D-DIGE.87 Image analysis of the latter provides a more sensitive quantification method, as up to 10-fold lower amount of samples can be applied. In addition, 2D-DIGE allows co-running of different samples in the same gel, labeled with distinct fluorescent dies (i.e., Cy3, Cy2, Cy5), and might also include an internal control for cross-gel comparison purposes. Those techniques have significantly improved in the previous years with respect to reproducibility and robustness, allowing better comparison between samples and across different laboratories.

Furthermore, mass spectrometry (MS) revolutionized proteomic studies when combined with the 2DE/2D-DIGE workflow, improving sensitivity for identification of differentially expressed proteins, by measuring molecular mass-to-charge ratio of ions (m/z).88 Protein spots, excised from the gel, are digested (i.e., trypsin) and masses of these peptides measured on MS instruments, providing a peptide mass fingerprint of each protein, which is then compared with an in silico-digested database. Further fragmentation of each peptide, performed on an MS/MS instrument, provides the sequence of that peptide, assisting in protein identification. Disadvantages of 2DE/MS combination include the incompatibility to very low or very high molecular weight or isoelectric point, in addition to those proteins with low abundance, which will not be spotted.89 Nevertheless, several proteome studies in schizophrenia were performed using proteomic screening approaches such as 2DE/2D-DIGE, providing large-scale data on the pathophysiology of the disease.41,55,56,58,61,66,90

Hence, schizophrenia and other psychiatric disorder studies have intensively used shotgun proteomics for the analysis of peptides and proteins for profiling, and for quantification of protein modification analysis.54,59,72,75,77,91 For shotgun proteomics, proteins are first digested into peptides (i.e., using trypsin, as previously), which are next separated by high-performance liquid chromatography online-connected to a hybrid MS, providing a gel-free proteomic system (LC-MS/MS). Shotgun proteomics lead to the possibility of identifying more proteins, increasing sampling of low abundant and extreme-sized proteins.82 Most proteomic studies on schizophrenia have used label-free methods for quantification,59,75,92 which assume chromatographic peak areas correlated to the concentration of peptides,93and is one of the simplest ways to compare proteomics, allowing comparison among several samples at once.94Nevertheless, both in vitro and in vivo stable isotopic labeling methods are available in shotgun proteomics, for quantification accuracy of protein concentrations simultaneously in several biological samples. In vitroapproaches include isobaric tags for relative and absolute quantification95 and isotope-coded protein labeling, whereas in vivo metabolic methods, such as stable isotope labeling by amino acids in cell culture96 and stable isotope labeling in mammals, have been used in proteomic quantification.97 Some have been applied to neuropsychiatric disorders, in postmortem brains and CSF,54,57,98 and in animal and cell studies99,100 on proteomic research.

The power to identify and quantify proteins and protein sets at high resolution, among multiple samples, is essential to understand large case studies in biomedical research. Recent advances have been made in MS-based techniques, such as selected reaction monitoring/multiple reaction monitoring, which has just emerged as a promising technology for a more precise MS-based quantification of targeted protein,101,102 and was awarded Nature’s Method of the Year in 2012 on biological research methods.103 Selected reaction monitoring is specific, accurate, and sensitive, as it selects proteotypic peptides—those that uniquely identify the targeted protein—for its analysis, which might overcome several current validation issues, such as semiquantitative western blotting techniques, availability, and specificity.104 Furthermore, this ability to quantify specific proteins across several samples is particularly interesting with regard to biomarkers, as clinical validation of biomarker signatures for a given disease must be tested over a large sample set to achieve satisfactory statistical power. Indeed, proteomic studies in psychiatric disorders slowly start to validate pathways and biological functions that were found differentially expressed by selected reaction monitoring.105,106,107,108

Likewise, other proteomic methodologies have been extensively applied to schizophrenia research in order to discover and validate biomarkers, such as multiplex immunoassays,69 which use multiplexed dye-coded microspheres of selected protein sets, thus providing profile studies of cytokines, growth factors, or metabolic pathways, from blood serum or CSF samples.70,109,110,111Aiming to reach the broader spectrum of protein visualization, concerns regarding the possibility to obtain sub-proteomes (using fractionation methods)112 by depleting high-abundant proteins or enriching a group of proteins in a sample should be part of the design and technique choice. Protein separation and quantification using SELDI-TOF-MS ProteinChip analysis or metal ion affinity chromatography to select proteins from a mixture have been used in schizophrenia research lately.65,68,72 Regardless of the protein analysis method, study design and sample preparation choice are crucial steps in proteomic studies. Platforms using reduced number of analytes, but a broader number of clinical samples, provide a precise statistical interpretation.

Indeed, statistics and bioinformatics are of extreme importance for proteomic studies, as different types of assays (2DE, shotgun-MS, or multiplex immunoassays) are required to precisely quantify changes in expression of hundreds (or thousands) of proteins. Therefore, those fields are improving, together with the development of new tools and methods for proteomic analysis, offering better algorithms and image analysis tools, in order to provide a more robust analysis from the growing number of data generated.

What do proteomics tell us about schizophrenia?

Proteomic technologies, mostly focusing on mass spectrometric analysis, are a valuable tool in psychiatric research. A simple search on PubMed using the terms ‘proteomics or proteome and schizophrenia’ provides a total of 218 articles since the first article on proteomics of schizophrenia in the beginning of the 2000s.56 Out of them, 124 articles (and growing) were published within the last 5 years (2010–2014) on human and animal studies, including some reviews, showing considerable increase in awareness of the importance of proteomics in the study of schizophrenia. We have focused, for the purpose of the review, on proteomic studies on human samples of schizophrenia patients compared with controls, from the last 5 years.

These studies, which are summarized in Table 1, have been using proteomic screening approaches such as shotgun-MS (10/23), 2DE/DIGE (7/23), and multiplex immunoassays (10/23), alone or combined. Although postmortem brains are the main studied tissue in schizophrenia research,57,58,59,61,113 influences of chronic medication or sample heterogeneity and age have impaired some interpretation of the molecular differences found in postmortem brain tissue of schizophrenia patients compared with control subjects.114Thus, current studies have been mostly focusing on more accessible peripheral tissues, with a preference for blood serum and plasma,72,73,109,115 and CSF,65,66 although there are studies on skin fibroblasts75 and saliva as well.76Those have become the main tissues used in proteomic studies of schizophrenia because of the possibility of multiple sampling, thus providing better characterization of disease onset, development, and response to treatment. This broader characterization could lead to a more complete understanding of the disease and to development of diagnostic/prognostic biomarkers. Indeed, an analysis of proteins that are common to brain, CSF, and blood samples from at least two studies presented in Table 1, using Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Qiagen, Redwood, CA, USA; www.ingenuity.com—Figure 1), shows biomarker candidates of psychiatric disorders and their interactions, and is further discussed.

Table 1: Human proteomic studies from the last 5 years of different tissues and cells in schizophrenic patients

Differentially expressed proteins in schizophrenia proteomic studies have been found to be involved in neuronal transmission, synaptic plasticity, and neurites outgrowth, including several cytoskeletal constituents. Most significant proteomic changes included downregulation of neuroreceptors such as NMDA receptors and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA), in addition to glutamatergic signaling molecules, such as neurofilaments (NEFL and NEFM), glutamate-ammonia ligase (GLUL), and guanine nucleotide-binding proteins (G proteins) (GNB1), or dihydropyrimidinase-related protein 2 (DPYSL2), which are involved in synaptic function, axon guidance, and signal transduction impairment in schizophrenia.59,113NEFL, in addition to its role in neuronal morphogenesis, is directly associated with NMDA receptors. NMDAR hypofunction was associated with neurotransmitter dysfunction in NR1 transgenic mice,105 with variations in bioactive peptides and proteins. As GLUL is responsible for removing glutamate from neuronal synapses, it is most likely involved in glutamate imbalance in schizophrenia.2

Other proteins related to NMDA functionality and synaptic plasticity, such as MAPK3, SYNPO, CYFIP2, VDAC, CAMK2B, PRDX1, and ESYT, were also observed differentially expressed in postsynaptic density-enriched samples of postmortem brain tissues.59 Data corresponding to a genomic study of schizophrenia34found an excess of copy number variants in schizophrenia, confirming several of the proteins differentially regulated with functions in the postsynaptic membrane.

Calcium homeostasis and signaling

Calcium signaling has also been found to be differentially regulated in schizophrenia proteomic studies.54,76,113,116Calcium is a pivotal metabolite for the dopaminergic hypothesis in schizophrenia, mainly because it has a central role in the function of dopamine receptors D1 and D2.117 Proteins such as calmodulin (CALM1, CALM2), calcium/calmodulin-dependent protein kinase II (CAMK2B, CAMK2D, CAMK2G), voltage-dependent anion channels (VDAC1, VDAC2), and the plasma membrane calcium-transporting ATPase 4 (PMCA-4) are some of the calcium-related proteins found downregulated in the brains of schizophrenia patients.59,116 Some proteins were found differentially expressed in secretion fluids of schizophrenic patients—for example, calmodulin-like proteins and the S100 family of calcium-binding proteins (S100A6, S100A12)—such as in eccrine sweat118 and saliva.76 Complementing these findings, S100B was found downregulated in the nuclear proteome of schizophrenia corpus callosum.119 In addition, calcium activated differential expression of calmodulin-dependent protein kinase II (CAMK2), and calcineurin A in phencyclidine-treated rats.113

Energy metabolism

The brain has a high glucose uptake to supply its major metabolic activity rate. Thus, one of the most consistent dysfunctions underlying the pathophysiology of schizophrenia is in energy metabolism pathways, along with mitochondrial dysfunction and oxidative stress.120,121Glucose metabolism is confirmed by hyperglycemia, impaired glucose tolerance, and/or insulin resistance in first-onset, antipsychotic, naive schizophrenic patients.110,122 Numerous proteomic studies have identified the glycolysis–gluconeogenesis pathway as being consistently disrupted both in brain and CSF,41,57,58,60,123 and is followed by peripheral tissues.77,90,113,120 The expression of proteins associated with the energy metabolism pathway, such as aldolase C (ALDOC), enolase 1 (ENO1), neuronal enolase 2 (ENO2), lactate dehydrogenase B (LDHB), phosphoglycerate mutase 1 (brain) (PGAM1), phosphoglycerate kinase 1 (PGK1), pyruvate kinase isozyme R/L [PKLR], and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), are often significantly deregulated in schizophrenic patients compared with controls.55,77,120 The most consistent differentially expressed enzyme is aldolase C (ALDOC), which was found altered in several brain samples58,61,66 and also as a marker on blood serum samples.77 Likewise, pyruvate, the final product of glycolysis, and NADPH have been quantified in lower amounts in schizophrenic samples compared with controls, in the thalamus,57 and is replicated in phencyclidine-treated rats, a model of schizophrenia research.113 Whereas schizophrenia seems to be more associated with glycolysis, major depressive disorders are likely to be more associated with oxidative phosphorylation.124

DISC1, a major risk factor of the schizophrenia-susceptibility gene candidate,22 can affect mitochondrial morphology and axonal trafficking.125,126 Alterations of mitochondria morphology were reinforced by the imbalance of the oxidative phosphorilation system, including proteins such as NADH dehydrogenases (i.e., NDUFA1, NDUFV2, NDUFS3, NDUFB5), and ATPases (ATP5B, ATP6V1B2, ATP6V1A1), which have been previously shown altered in animal models of schizoprenia,113,127,128,129 but also had significant regulation in human brains.57,59 Other molecules such as dopamine have been shown to inhibit electron transport chain complex I (NADH dehydrogenase).130

Oxidative stress

This overall imbalance of mitochondrial energy metabolism, associated with elevated calcium, leads to hazardous ROS concentrations and oxidative stress events in brain cells.131 The resultant ROS may cause oxidative damage in cellular DNA, RNA, proteins, and membrane lipids. Proteomics of the brain have shown several enzymes involved in redox activities (responsible for removing ROS and protecting cells against oxidative injury) to be differentially expressed in schizophrenia brain tissues. Proteins such as superoxide dismutase (which catalyze the dismutation of superoxide (O2−) into oxygen and hydrogen peroxide), peroxiredoxins (PRDX1, PRDX2, and PRDX3) (which are responsible for reducing hydrogen peroxide), glutathione S-transferases (i.e., GSTM3, GSTTLp28, and GSTP1) (which are a family of multifunctional enzymes involved in cellular detoxification, glutathione reduction, and neutralization of ROS), and NADPH-dependent oxidoreductases such as carbonyl reductases (CBR1 and CBR2) and quinoid dihydropteridine reductase (QDPR) (which might be involved in the NADP/NADPH imbalance observed in the thalamus) were often found regulated in brain tissue,41,57,58,59,61,113 but could also be detected to be differentially regulated in peripheral tissues such as blood and fibroblast samples .70,73,75,132 Proteomics and combined metabolomics support evidence that slight imbalance in energy glucose metabolism, disrupting mitochondria and the oxidative phosphorylation system, results in compromised ATP production and oxidative stress, which is central in the pathophysiology of schizophrenia.41,113,133

Cytoskeleton

Cytoskeleton constituents are proteins that have shown broad differential expressions in schizophrenia—namely, microtubules such as tubulins (TBA1B, TUBB2A), microfilaments such as actins (ACTG1, ACTB) and actin-binding proteins such as tropomyosins (TPM1, TPM2, TPM3, TPM4), and intermediate filaments (i.e., GFAP, vimetin) and endocytosis proteins, such as dynamin (DNM1), a protein involved in clathrin-mediated endocytosis and other vesicular trafficking processes.55,58,59,113,134,135 Such modifications impact the cellular structure, axonal function, and neurite outgrowth, influencing synaptic plasticity and metabolism, all significantly influencing disturbed cytoskeleton arrangement in schizophrenia.44 Protein components of the cytoskeleton, such as the above-mentioned neurofilaments M and L (NEFL, NEFM) and DPYSL2, a regulator of cytoskeleton remodeling, have a role in axon guidance, neuronal growth, and cell migration. Glial fibrillary acidic protein (GFAP), the major intermediate filament of astrocytes, was found to be strongly regulated in brain tissues, both up- and downexpressed, indicating a precise protein expression across the brain.55,59,61,135,136 In addition, actin was often found downregulated in brain tissues,41,61,62,75 but was upregulated in fibroblasts75 or liver74 of schizophrenic patients.

Immune system and inflammation

Several abnormalities were found in schizophrenia proteomics, including changes in immune- and inflammation-related pathways in first-onset schizophrenic patients compared with controls. Molecules such as α-defensins (DEFA1, DEFA2, DEFA3, DEFA4), migration inhibitory factor, and several interleukins (IL-1ra, IL-8, IL-10, IL-15, IL-16, IL-17, and IL-18), including growth factors such as brain-derived neurotrophic factor, have been differentially regulated in blood samples from schizophrenic patients compared with controls.70,73,76,109,115,132 In addition, extracellular calcium-binding S100A12 exhibits cytokine-like characteristics, recruiting inflammatory cells to the sites of tissue damage. Indeed, anti-inflammatory treatment with cyclooxygenase-2 (COX-2) inhibitors has shown diminished schizophrenic behavior by blocking the synthesis of proinflammatory prostaglandins.137 Multiplex immunoassay profiling studies of blood serum have found numerous components of inflammation signaling pathways.109,111,115 Levels of anti-inflammatory cytokines IL-1ra and IL-10 were decreased after treatment with atypical antipsychotics, which correlated with symptom improvement.109 In addition, profiling studies using a subset of cytokines found increased levels of interleukins (i.e., IL-1β) in the cerebrospinal fluid of first-episode schizophrenic patients, indicative of immune system activation in the brain of some patients.138 Therefore, a proper subset of those altered molecular inflammatory molecules could be included in a sensitive and specific biomarker panel, both for diagnosis and treatment follow-up response.

An overview

Diverse proteomic techniques provided non-biased screening analyses of postmortem brain tissue from schizophrenic patients, and insights into pathways affected in the disease.10,57 In addition, more accessible tissues, such as cerebrospinal fluid, blood serum and plasma, and others such as fibroblasts, liver, and urine,57,66,70,74,75,139 have complemented those findings, suggesting several proteins that could be used as biomarkers to improve diagnosis. We have not gone through the details of the role of oligodendrocytes in schizophrenia, as these were recently tackled somewhere else,140,141,142 although these are as important as all that are listed here.

Proteomic insights from naive first-onset patients’ impaired protein pathways confirm patterns of disease onset, which along with genetic predisposition could be used as biomarkers for stratification of patients, improving the diagnosis and treatment classification. Also, this valuable information can lead to a more individualized medication, selected according to specific molecular dysfunctions and phenotype observed in schizophrenic individuals. Understanding the pathways affected by medication might also lead to reliable analytical platforms to evaluate individual response to treatment in a personalized-medicine mode. Moreover, the ability to monitor levels of molecules in noninvasive body fluids, such as saliva, urine, or blood serum or plasma, is a great advance. In addition, knowledge of gene–protein pathway networks affected and impaired by the disease can give clues for the development of new and more efficient targeted drugs to those relevant pathways.51,121

Perspectives

Psychiatric disorders are one of the biggest burdens to society,3 and consequently one of the most challenging fields of medical research, with complex and multifactorial characteristics, along with genetic, neurodevelopmental, environmental, and molecular components. Hence, proteomics can add valuable insights into revealing psychiatric disorder connections, as it is closely linked to phenotype, and, by definition, proteomics constitute one of the most suitable approaches for this purpose.143

In 2010, the Human Proteome Organization (HUPO) started a project aiming to map the entire human proteome, the Human Proteome Project (HPP) initiative, with joint initiatives such as the Chromosome-centric Human Proteome Project.144,145,146 Thus, at the beginning of 2014, two extensive drafts of this map were released,79,80 showing progress in the identification of proteins from high-quality proteomic data to complement genomic annotation. The Human Brain Proteome Project (HBPP) initiative, specifically addressing the proteomic landscape of the human brain, aims to study individuals affected by neurodegenerative diseases, understanding its many different cell types and their particular structure at the cellular and tissue level.147,148 Another main focus is to untangle the human plasma proteome149 on health and disease, to support biomarker validation and development of new tools for diagnosis, disease progression, and medication efficiency, considering the confounding factors present in those body fluids.

From the schizophrenia research point of view, this are exciting news, because of the potential of information that can be extracted, as, regardless of efforts in the search for biomarkers, by investigating the transcriptome and proteome in the post-genomic era, schizophrenia is one more psychiatric disorder without a reliable marker. Those recent advances in ‘omics’ technologies, such as genomics, transcriptomics, proteomics, and metabolomics, which are not only expanding coverage and resolution but also becoming cheaper and more accessible, present new prospects for a global comprehension of biological characteristics of disease mechanisms.150

While genomic and transcriptomic technologies have achieved single-nucleotide resolution, the protein coverage of the amino-acid sequence is still restricted. State-of-the-art shotgun mass spectrometry has improved immensely, such as targeted proteomic measurements, and is useful for biomarker identification. Although the detection of some protein variants, such as differential splice products and posttranslational modifications, remains a challenge for proteomics to get a more comprehensive picture of the whole proteome using a systematic approach. This high-throughput investigation of nucleic acids, proteins, and metabolites from particular tissues and cells provides essential data, which is basic to system biology studies, in order to create integral models of cellular processes.151 Therefore, integrating biological data from omics studies to the expertise of complementary disciplines such as mathematics, physics, and computational sciences, toward better conceptual analysis and predictive models, provides new tools for understanding biological systems at different levels. Hence, we can analyze the cellular space-time and hierarchical organization,152 aiming for complete understanding of psychiatric diseases and identifying candidate biomarkers, especially before and after the onset of clinical manifestations, as well as target metabolic pathways impaired and/or affected by antipsychotics, in order to distinguish subgroups of patients who respond to medication on the basis of their molecular profiles.51

Plasticity is a critical factor enabling stem cells to contribute to the development and regeneration of tissues. In the mammalian central nervous system (CNS), neural stem cells (NSCs) that are defined by their capability for self-renewal and differentiation into neurons and glia, are present in the ventricular neuroaxis throughout life. However, the differentiation potential of NSCs changes in a spatiotemporally regulated manner and these cells progressively lose plasticity during development. One of the major alterations in this process is the switch from neurogenesis to gliogenesis. NSCs initiate neurogenesis immediately after neural tube closure and then turn to gliogenesis from midgestation, which requires an irreversible competence transition that enforces a progressive reduction of neuropotency. A growing body of evidence indicates that the neurogenesis-to-gliogenesis transition is governed by multiple layers of regulatory networks consisting of multiple factors, including epigenetic regulators, transcription factors, and non-coding RNA (ncRNA). In this review, we focus on critical roles of microRNAs (miRNAs), a class of small ncRNA that regulate gene expression at the post-transcriptional level, in the regulation of the switch from neurogenesis to gliogenesis in NSCs in the developing CNS. Unraveling the regulatory interactions of miRNAs and target genes will provide insights into the regulation of plasticity of NSCs, and the development of new strategies for the regeneration of damaged CNS.

A research team led by scientists at SUNY Downstate Medical Center has identified a brain receptor that appears to initiate adolescent synaptic pruning, a process believed necessary for learning, but one that appears to go awry in both autism and schizophrenia.

Sheryl Smith, Ph.D., professor of physiology and pharmacology at SUNY Downstate, explained that “Memories are formed at structures in the brain known as dendritic spines that communicate with other brain cells through synapses. The number of brain connections decreases by half after puberty, a finding shown in many brain areas and for many species, including humans and rodents.”

This process is referred to as adolescent “synaptic pruning” and is thought to be important for normal learning in adulthood. Synaptic pruning is believed to remove unnecessary synaptic connections to make room for relevant new memories, but because it is disrupted in diseases such as autism and schizophrenia, there has recently been widespread interest in the subject.

“Our report is the first to identify the process which initiates synaptic pruning at puberty. Previous studies have shown that scavenging by the immune system cleans up the debris from these pruned connections, likely the final step in the pruning process,” added Dr. Smith. “Working with a mouse model we have shown that, at puberty, there is an increase in inhibitory GABA [gamma-aminobutyric acid] receptors, which are targets for brain chemicals that quiet down nerve cells. We now report that these GABA receptors trigger synaptic pruning at puberty in the mouse hippocampus, a brain area involved in learning and memory.”

The study (“Synaptic Pruning in the Female Hippocampus Is Triggered at Puberty by Extrasynaptic GABAA Receptors on Dendritic Spines”) is published online in eLife.

Dr. Smith noted that by reducing brain activity, these GABA receptors also reduce levels of a protein in the dendritic spine, kalirin-7, which stabilizes the scaffolding in the spine to maintain its structure. Mice that do not have these receptors maintain the same high level of brain connections throughout adolescence.

Dr. Smith pointed out that the mice with too many brain connections, which do not undergo synaptic pruning, are able to learn spatial locations, but are unable to relearn new locations after the initial learning, suggesting that too many brain connections may limit learning potential.

These findings may suggest new treatments targeting GABA receptors for “normalizing” synaptic pruning in diseases such as autism and schizophrenia, where synaptic pruning is abnormal. Research has suggested that children with autism may have an over-abundance of synapses in some parts of the brain. Other research suggests that prefrontal brain areas in persons with schizophrenia have fewer neural connections than the brains of those who do not have the condition.

Synaptic pruning in the female hippocampus is triggered at puberty by extrasynaptic GABAAreceptors on dendritic spines

JulieParato

Department of Physiology and Pharmacology, SUNY Downstate Medical Center, Brooklyn, United States

Adolescent synaptic pruning is thought to enable optimal cognition because it is disrupted in certain neuropathologies, yet the initiator of this process is unknown. One factor not yet considered is the α4βδ GABAA receptor (GABAR), an extrasynaptic inhibitory receptor which first emerges on dendritic spines at puberty in female mice. Here we show that α4βδ GABARs trigger adolescent pruning. Spine density of CA1 hippocampal pyramidal cells decreased by half post-pubertally in female wild-type but not α4 KO mice. This effect was associated with decreased expression of kalirin-7 (Kal7), a spine protein which controls actin cytoskeleton remodeling. Kal7 decreased at puberty as a result of reduced NMDAR activation due to α4βδ-mediated inhibition. In the absence of this inhibition, Kal7 expression was unchanged at puberty. In the unpruned condition, spatial re-learning was impaired. These data suggest that pubertal pruning requires α4βδ GABARs. In their absence, pruning is prevented and cognition is not optimal.

Searches Related to Synaptic Pruning in the Female Hippocampus Is Triggered at Puberty by Extrasynaptic GABAA Receptors on Dendritic Spines

Prof. Dr. Olivia Masseck, who led the work, researches the causes of anxiety and depression. For more than 60 years, researchers have been hypothesising that the diseases are caused by, among other factors, changes to the level of serotonin. But understanding how the serotonin system works is quite difficult, says Masseck, who became junior professor for Super-Resolution Fluorescence Microscopy at RUB in April 2016.

The number of receptors for serotonin in the brain amounts to 14, occurring in different cell types. Consequently, determining the functions that different receptors fulfill in the individual cell types is a complicated task. If, however, the proteins are coupled to light-sensitive pigments, they can be switched on and off with light of a specific color at high spatial and temporal precision. Masseck used this method, known as optogenetics, to characterize, for example, the properties of different light-sensitive proteins and identified the ones that are best suited as optogenetic tools. She has analyzed several light-sensitive varieties of the serotonin receptors 5-HT1A and 5-HT2C in great detail. Together with her collaborators, she has demonstrated in several studies that both receptors can control the anxiety behavior of mice.

To investigate the serotonin system more closely, Masseck and her research team is currently developing a sensor that is going to indicate the neurotransmitter in real time. One potential approach involves the integration of a modified form of a green fluorescent protein into a serotonin receptor.

In a brain slice, Olivia Masseck measures the activity of nerve cells in which she switches on their receptors using light stimulation. Via the pipette a red dye diffuses into the cell, rendering them visible in the brain slice. (Copyright: RUB, Damian Gorczany)

This protein produces green light only if it is embedded in a specific spatial structure. If a serotonin molecule binds to a receptor, the receptor changes its three-dimensional conformation. The objective is to integrate the fluorescent protein in the receptor so that its spatial structure changes together with that of the receptor when it binds a serotonin molecule, in such a way that the protein begins to glow.

New optogenetic tools by Julia Weiler
Anxiety and depression are two of the most frequently occurring mental disorders worldwide. Light-activated nerve cells may indicate how they are formed.

Statistically, every fifth individual suffers from depression or anxiety in the course of his or her life. The mechanisms that trigger these disorders are not yet fully understood, despite the fact that researchers have been studying the hypothesis that one of the underlying cause are changes to the level of the neurotransmitter serotonin for 60 years.

“Unfortunately, it is very difficult to understand how the serotonin system works,” says Prof Dr Olivia Masseck, who is junior professor for Super-Resolution Fluorescence Microscopy since the end of April 2016. She intends to fathom the mysteries of the complex system. The number of receptors for the neurotransmitter in the brain amounts to 14 in total, and they occur in different cell types. Consequently, determining the functions that different receptors fulfil in the individual cell types is a complicated task.

In order to fathom the purpose of such receptors, researchers used to observe which functions were inhibited after they had been activated or blocked with the aid of pharmaceutical drugs. However, many substances affect not just one receptor, but several at the same time. Moreover, researchers cannot tell receptors in the individual cell types apart when pharmaceutical drugs have been applied. “It had been impossible to study serotonin signalling pathways at high spatial and temporal resolution,” adds Masseck. Until the development of optogenetics.

“This method has revolutionised neuroscience,” says Olivia Masseck, whose collaborator Prof Dr Stefan Herlitze was one of the pioneers in this field. Optogenetics allows precise control over the activity of specific nerve cells or receptors with light. What sounds like science fiction, is routine at RUB’s Neuroscience Research Department. Masseck: “Until now, we had been passive observers, and monitoring cell activity was all we could do; now, we are able to manipulate it precisely.”

The researcher from Bochum is mainly interested in the 5-HT1A and 5-HT1B receptors, the so-called autoreceptors of the serotonin system. They occur in serotonin-producing cells, where they regulate the amount of released neurotransmitters; that means they determine the serotonin level in the brain.

Normally, 5-HT1A and 5-HT1B are activated when a serotonin molecule bonds to the receptor. The docking triggers a chain reaction in the cell. The effects of this signalling cascade include a reduced activity of the neural cell, which releases less neurotransmitter.

By modifying certain brain cells in the brains of mice, Olivia Masseck successfully activated the 5-HT1Areceptor without the aid of serotonin. She combined it with a visual pigment – so-called opsin. More specifically, she utilised blue or red visual pigments from the cones responsible for colour vision. This is how she generated a serotonin receptor that she could switch on with red or blue light. This method enables the RUB researcher to identify the role the 5-HT1A receptor plays in anxiety and depression.

To this end, she delivered the combined protein made up of light-sensitive opsin and serotonin receptor into the brain of mice using a virus that had been rendered harmless. Like a shuttle, it transports genetic information which contains the blueprint for the combined protein. Once injected into brain tissue, the virus implants the gene for the light-activated receptor in specific nerve cells. There, it is read, and the light-activated receptor is incorporated into the cell membrane.

The researcher was now able to switch the receptors on and off in a living mouse using light. She analysed in what way this manipulation affected the animals’ behaviour in an anxiety test, i.e. Open Field Test. For the purpose of the experiment, she placed individual mice in a large, empty Plexiglas box.

Under normal circumstances, the animals avoid the centre of the brightly-lit box, because it doesn’t offer any cover. Most of the time, they stay close to the walls. When Olivia Masseck switched on the 5-HT1Areceptor using light, the behaviour of the mice changed. They were less anxious and spent more time in the middle of the Plexiglas box.

These results were confirmed in a further test. Olivia Masseck stopped the time it took the mice to eat a food pellet in the middle of a large Plexiglas box. Normal animals waited between six and seven minutes before they ventured into the centre to feed. However, mice whose serotonin receptor was switched on started to feed after one or two minutes. “This is important evidence indicating that the 5-HT1A receptor signalling pathway in the serotonin system is linked to anxiety,” concludes Masseck.

In the next step, the researcher intends to find out in what way depressive behaviour is affected by the activation of the 5-HT1A receptor. “If the animals are exposed to chronic stress, they develop symptoms similar to those in humans with depression,” describes Masseck. “They might, for example, withdraw from social interactions.”

However, just like in humans, this applies to only a certain percentage of the mice. “Not every individual who suffers from chronic stress or experiences negative situations develops depression,” points out Masseck. What happens in the serotonin system of animals that are susceptible to depression, as opposed to that of animals that do not present any depressive symptoms? This is what the researcher intends to find out by deploying the optogenetic methods described above; in addition, she is currently developing a custom-built serotonin sensor.

Olivia Masseck’s assumption is that her findings regarding the neuronal circuits and molecular mechanisms of anxiety and depression are applicable to humans. Mice have similar cell functions, and their nervous system has a similar structure. The neuroscientist expects that optogenetics will one day be deployed in human applications.

“Genetic manipulation of cells for the purpose of controlling them with light might sound like science fiction,” she says, “but I am convinced that optogenetics will be used in human applications in the next decades.” It could, for example, be utilised for deep brain stimulation in Parkinson’s patients, because it facilitates precise activation of the required signalling pathways, with fewer side effects, at that.

“In the first step, optogenetics will be used in therapy of retinal diseases,” believes Olivia Masseck. Researchers are currently conducting experiments aiming at restoring the visual function in blind mice.

Olivia Masseck is aware that her research raises ethical questions. “We have to discuss in which applications we want or don’t want to use these techniques,” she says. Her research demonstrates how easily the lines between science-fiction films and scientific research can blur.

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The author and his father have seen several relatives succumb to mental illness.CREDIT PHOTOGRAPH BY DAYANITA SINGH FOR THE NEW YORKER

In the winter of 2012, I travelled from New Delhi, where I grew up, to Calcutta to visit my cousin Moni. My father accompanied me as a guide and companion, but he was a sullen and brooding presence, lost in a private anguish. He is the youngest of five brothers, and Moni is his firstborn nephew—the eldest brother’s son. Since 2004, Moni, now fifty-two, has been confined to an institution for the mentally ill (a “lunatic home,” as my father calls it), with a diagnosis of schizophrenia. He is kept awash in antipsychotics and sedatives, and an attendant watches, bathes, and feeds him through the day.

My father has never accepted Moni’s diagnosis. Over the years, he has waged a lonely campaign against the psychiatrists charged with his nephew’s care, hoping to convince them that their diagnosis was a colossal error, or that Moni’s broken psyche would somehow mend itself. He has visited the institution in Calcutta twice—once without warning, hoping to see a transformed Moni, living a secretly normal life behind the barred gates. But there was more than just avuncular love at stake for him in these visits. Moni is not the only member of the family with mental illness. Two of my father’s four brothers suffered from various unravellings of the mind. Madness has been among the Mukherjees for generations, and at least part of my father’s reluctance to accept Moni’s diagnosis lies in a grim suspicion that something of the illness may be buried, like toxic waste, in himself.

Rajesh, my father’s third-born brother, had once been the most promising of the Mukherjee boys—the nimblest, the most charismatic, the most admired. But in the summer of 1946, at the age of twenty-two, he began to behave oddly, as if a wire had been tripped in his brain. The most obvious change in his personality was a volatility: good news triggered uncontained outbursts of joy; bad news plunged him into inconsolable desolation. By that winter, the sine curve of Rajesh’s psyche had tightened in its frequency and gained in its amplitude. My father recalls an altered brother: fearful at times, reckless at others, descending and ascending steep slopes of mood, irritable one morning and overjoyed the next. When Rajesh received news of a successful performance on his college exams, he vanished, elated, on a two-night excursion, supposedly “exercising” at a wrestling camp. He was feverish and hallucinating when he returned, and died of pneumonia soon afterward. Only years later, in medical school, did I realize that Rajesh was likely in the throes of an acute manic phase. His mental breakdown was the result of a near-textbook case of bipolar disorder.

Jagu, the fourth-born of my father’s siblings, came to live with us in Delhi in 1975, when I was five years old and he was forty-five. His mind, too, was failing. Tall and rail thin, with a slightly feral look in his eyes and a shock of matted, overgrown hair, he resembled a Bengali Jim Morrison. Unlike Rajesh, whose illness had surfaced in his twenties, Jagu had been troubled from his adolescence. Socially awkward, withdrawn from everyone except my grandmother, he was unable to hold a job or live by himself. By 1975, he had visions, phantasms, and voices in his head that told him what to do. He was still capable of extraordinary bursts of tenderness—when I accidentally smashed a beloved Venetian vase at home, he hid me in his bedclothes and informed my mother that he had “mounds of cash” stashed away, enough to buy “a thousand” replacement vases. But this episode was symptomatic: even his love for me extended the fabric of his psychosis and confabulation.

Unlike Rajesh, Jagu was formally diagnosed. In the late nineteen-seventies, a physician in Delhi examined him and determined that he had schizophrenia. But no medicines were prescribed. Instead, Jagu continued to live at home, half hidden away in my grandmother’s room. (As in many families in India, my grandmother lived with us.) For nearly a decade, she and my father maintained a fragile truce, with Jagu living under her care, eating meals in her room and wearing clothes that she stitched for him. At night, when Jagu was consumed by his fears and fantasies, she put him to bed like a child, with her hand on his forehead. She was his nurse, his housekeeper, his only friend, and, more important, his public defender. When my grandmother died, in 1985, Jagu joined a religious sect in Delhi and disappeared, until his death, a dozen years later.

……

at schizophrenia runs in families was evident even to the person who first defined the illness. In 1911, Eugen Bleuler, a Swiss-German psychiatrist, published a book describing a series of cases of men and women, typically in their teens and early twenties, whose thoughts had begun to tangle and degenerate. “In this malady, the associations lose their continuity,” Bleuler wrote. “The threads between thoughts are torn.” Psychotic visions and paranoid thoughts flashed out of nowhere. Some patients “feel themselves weak, their spirit escapes, they will never survive the day. There is a growth in their heads. Their bones have turned liquid; their hearts have turned into stone. . . . The patient’s wife must not use eggs in cooking, otherwise he will grow feathers.” His patients were often trapped between flickering emotional states, unable to choose between two radically opposed visions, Bleuler noted. “You devil, you angel, you devil, you angel,” one woman said to her lover.

Bleuler tried to find an explanation for the mysterious symptoms, but there was only one seemingly common element: schizophrenic patients tended to have first-degree relatives who were also schizophrenic. He had no tools to understand the mechanism behind the heredity. The word “gene” had been coined just two years before Bleuler published his book. The notion that a mental illness could be carried across generations by unitary, indivisible factors—corpuscles of information threading through families—would have struck most of Bleuler’s contemporaries as mad in its own right. Still, Bleuler was astonishingly prescient about the complex nature of inheritance. “If one is looking for ‘theheredity,’ one can nearly always find it,” he wrote. “We will not be able to do anything about it even later on, unless the single factor of heredity can be broken down into many hereditary factors along specific lines.”

In the nineteen-sixties, Bleuler’s hunch was confirmed by twin studies. Psychiatrists determined that if an identical twin was schizophrenic the other twin had a forty-to-fifty-per-cent chance of developing the disease—fiftyfold higher than the risk in the general population. By the early two-thousands, large population studies had revealed a strong genetic link between schizophrenia and bipolar disorder. Some of the families described in these studies had a crisscrossing history that was achingly similar to my own: one sibling affected with schizophrenia, another with bipolar disorder, and a nephew or niece also schizophrenic.

“The twin studies clarified two important features of schizophrenia and bipolar disorder,” Jeffrey Lieberman, a Columbia University psychiatrist who has studied schizophrenia for thirty years, told me. “First, it was clear that there wasn’t a single gene, but dozens of genes involved in causing schizophrenia—each perhaps exerting a small effect. And, second, even if you inherited the entire set of risk genes, as identical twins do, you still might not develop the disease. Obviously, there were other triggers or instigators involved in releasing the illness.” But while these studies established that schizophrenia had a genetic basis, they revealed nothing about the nature of the genes involved. “For doctors, patients, and families in the schizophrenia community, genetics became the ultimate mystery,” Lieberman said. “If we knew the identity of the genes, we would find the causes, and if we found the causes we could find medicines.”

In 2006, an international consortium of psychiatric geneticists launched a genomic survey of schizophrenia, hoping to advance the search for the implicated genes. With 3,322 patients and 3,587 controls, this was one of the largest and most rigorous such studies in the history of the disease. Researchers scanned through the nearly seven thousand genomes to find variations in gene segments that were correlated with schizophrenia. This strategy, termed an “association study,” does not pinpoint a gene, but it provides a general location where a disease-linked gene may be found, like a treasure map with a large “X” scratched in a corner of the genome.

The results, reported in 2009 (and updated in 2014) in the journal Nature, were a dispiriting validation of Bleuler’s hunch about multiple hereditary factors: more than a hundred independent segments of the genome were associated with schizophrenia. “There are lots of small, common genetic effects, scattered across the genome,” one researcher said. “There are many different biological processes involved.” Some of the putative culprits made biological sense—if dimly. There were genes linked to transmitters that relay messages between neurons, and genes for molecular channels that move electrical signals up and down nerve cells. But by far the most surprising association involved a gene segment on chromosome 6. This region of the genome—termed the MHC region—carries hundreds of genes typically associated with the immune system.

“The MHC-segment finding was so strange and striking that you had to sit up and take notice,” Lieberman told me. “Here was the most definitive evidence that something in the immune system might have something to do with schizophrenia. There had been hints about an immunological association before, but this was impossible to argue with. It raised an endlessly fascinating question: what was the link between immune-response genes and schizophrenia?”

In the first years of her career in brain research, Beth Stevens thought of microglia with annoyance if she thought of them at all. When she gazed into a microscope and saw these ubiquitous cells with their spidery tentacles, she did what most neuroscientists had been doing for generations: she looked right past them and focused on the rest of the brain tissue, just as you might look through specks of dirt on a windshield.

“What are they doing there?” she thought. “They’re in the way.’”

Stevens never would have guessed that just a few years later, she would be running a laboratory at Harvard and Boston’s Children’s Hospital devoted to the study of these obscure little clumps. Or that she would be arguing in the world’s top scientific journals that microglia might hold the key to understanding not just normal brain development but also what causes Alzheimer’s, Huntington’s, autism, schizophrenia, and other intractable brain disorders.

Microglia are part of a larger class of cells—known collectively as glia—that carry out an array of functions in the brain, guiding its development and serving as its immune system by gobbling up diseased or damaged cells and carting away debris. Along with her frequent collaborator and mentor, Stanford biologist Ben Barres, and a growing cadre of other scientists, Stevens, 45, is showing that these long-overlooked cells are more than mere support workers for the neurons they surround. Her work has raised a provocative suggestion: that brain disorders could somehow be triggered by our own bodily defenses gone bad.

A type of glial cell known as an oligodendrocyte

In one groundbreaking paper, in January, Stevens and researchers at the Broad Institute of MIT and Harvard showed that aberrant microglia might play a role in schizophrenia—causing or at least contributing to the massive cell loss that can leave people with devastating cognitive defects. Crucially, the researchers pointed to a chemical pathway that might be targeted to slow or stop the disease. Last week, Stevens and other researchers published a similar finding for Alzheimer’s.

This might be just the beginning. Stevens is also exploring the connection between these tiny structures and other neurological diseases—work that earned her a $625,000 MacArthur Foundation “genius” grant last September.

All of this raises intriguing questions. Is it possible that many common brain disorders, despite their wide-ranging symptoms, are caused or at least worsened by the same culprit, a component of the immune system? If so, could many of these disorders be treated in a similar way—by stopping these rogue cells?

Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia’s strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.

Synapse loss in Alzheimer’s disease (AD) correlates with cognitive decline. Involvement of microglia and complement in AD has been attributed to neuroinflammation, prominent late in disease. Here we show in mouse models that complement and microglia mediate synaptic loss early in AD. C1q, the initiating protein of the classical complement cascade, is increased and associated with synapses before overt plaque deposition. Inhibition of C1q, C3 or the microglial complement receptor CR3, reduces the number of phagocytic microglia as well as the extent of early synapse loss. C1q is necessary for the toxic effects of soluble β-amyloid (Aβ) oligomers on synapses and hippocampal long-term potentiation (LTP). Finally, microglia in adult brains engulf synaptic material in a CR3-dependent process when exposed to soluble Aβ oligomers. Together, these findings suggest that the complement-dependent pathway and microglia that prune excess synapses in development are inappropriately activated and mediate synapse loss in AD.

Genome-wide association studies (GWAS) implicate microglia and complement-related pathways in AD (1). Previous research has demonstrated both beneficial and detrimental roles of complement and microglia in plaque-related neuropathology (2, 3); however, their roles in synapse loss, a major pathological correlate of cognitive decline in AD (4), remain to be identified. Emerging research implicates microglia and immune-related mechanisms in brain wiring in the healthy brain (1). During development, C1q and C3 localize to synapses and mediate synapse elimination by phagocytic microglia (5–7). We hypothesized that this normal developmental synaptic pruning pathway is activated early in the AD brain and mediates synapse loss.

Complex machinery

It’s not surprising that scientists for years have ignored microglia and other glial cells in favor of neurons. Neurons that fire together allow us to think, breathe, and move. We see, hear, and feel using neurons, and we form memories and associations when the connections between different neurons strengthen at the junctions between them, known as synapses. Many neuroscientists argue that neurons create our very consciousness.

Glia, on the other hand, have always been considered less important and interesting. They have pedestrian duties such as supplying nutrients and oxygen to neurons, as well as mopping up stray chemicals and carting away the garbage.

Scientists have known about glia for some time. In the 1800s, the pathologist Rudolf Virchow noted the presence of small round cells packing the spaces between neurons and named them “nervenkitt” or “neuroglia,” which can be translated as nerve putty or glue. One variety of these cells, known as astrocytes, was defined in 1893. And then in the 1920s, the Spanish scientist Pio del Río Hortega developed novel ways of staining cells taken from the brain. This led him to identify and name two more types of glial cells, including microglia, which are far smaller than the others and are characterized by their spidery shape and multiple branches. It is only when the brain is damaged in adulthood, he suggested, that microglia spring to life—rushing to the injury, where it was thought they helped clean up the area by eating damaged and dead cells. Astrocytes often appeared on the scene as well; it was thought that they created scar tissue.

This emergency convergence of microglia and astrocytes was dubbed “gliosis,” and by the time Ben Barres entered medical school in the late 1970s, it was well established as a hallmark of neurodegenerative diseases, infection, and a wide array of other medical conditions. But no one seemed to understand why it occurred. That intrigued Barres, then a neurologist in training, who saw it every time he looked under a microscope at neural tissue in distress. “It was just really fascinating,” he says. “The great mystery was: what is the point of this gliosis? Is it good? Is it bad? Is it driving the disease process, or is it trying to repair the injured brain?”

Barres began looking for the answer. He learned how to grow glial cells in a dish and apply a new recording technique to them. He could measure their electrical qualities, which determine the biochemical signaling that all brain cells use to communicate and coördinate activity.

“From the second I started recording the glial cells, I thought ‘Oh, my God!’” Barres recalls. The electrical activity was more dynamic and complex than anyone had thought. These strange electrical properties could be explained only if the glial cells were attuned to the conditions around them, and to the signals released from nearby neurons. Barres’s glial cells, in other words, had all the machinery necessary to engage in a complex dialogue with neurons, and presumably to respond to different kinds of conditions in the brain.

Why would they need this machinery, though, if they were simply involved in cleaning up dead cells? What could they possibly be doing? It turns out that in the absence of chemicals released by glia, the neurons committed the biochemical version of suicide. Barres also showed that the astrocytes appeared to play a crucial role in forming synapses, the microscopic connections between neurons that encode memory. In isolation, neurons were capable of forming the spiny appendages necessary to reach the synapses. But without astrocytes, they were incapable of connecting to one another.

Hardly anyone believed him. When he was a young faculty member at Stanford in the 1990s, one of his grant applications to the National Institutes of Health was rejected seven times. “Reviewers kept saying, ‘Nah, there’s no way glia could be doing this,’” Barres recalls. “And even after we published two papers in Science showing that [astrocytes] had profound, almost all-or-nothing effects in controlling synapses’ formation or synapse activity, I still couldn’t get funded! I think it’s still hard to get people to think about glia as doing anything active in the nervous system.”

Marked for elimination

Beth Stevens came to study glia by accident. After graduating from Northeastern University in 1993, she followed her future husband to Washington, D.C., where he had gotten work in the U.S. Senate. Stevens had been pre-med in college and hoped to work in a lab at the National Institutes of Health. But with no previous research experience, she was soundly rebuffed. So she took a job waiting tables at a Chili’s restaurant in nearby Rockville, Maryland, and showed up at NIH with her résumé every week.

After a few months, Stevens received a call from a researcher named Doug Fields, who needed help in his lab. Fields was studying the intricacies of the process in which neurons become insulated in a coating called myelin. That insulation is essential for the transmission of electrical impulses.

As Stevens spent the following years pursuing a PhD at the University of Maryland, she was intrigued by the role that glial cells played in insulating neurons. Along the way, she became familiar with other insights into glial cells that were beginning to emerge, especially from the lab of Ben Barres. Which is why, soon after completing her PhD in 2003, Stevens found herself a postdoc in Barres’s lab at Stanford, about to make a crucial discovery.

Barres’s group had begun to identify the specific compounds astrocytes secreted that seemed to cause neurons to grow synapses. And eventually, they noticed that these compounds also stimulated production of a protein called C1q.

Conventional wisdom held that C1q was activated only in sick cells—the protein marked them to be eaten up by immune cells—and only outside the brain. But Barres had found it in the brain. And it was in healthy neurons that were arguably at their most robust stage: in early development. What was the C1q protein doing there?

The answer lies in the fact that marking cells for elimination is not something that happens only in diseased brains; it is also essential for development. As brains develop, their neurons form far more synaptic connections than they will eventually need. Only the ones that are used are allowed to remain. This pruning allows for the most efficient flow of neural transmissions in the brain, removing noise that might muddy the signal.

But it was unknown how exactly the process worked. Was it possible that C1q helped signal the brain to prune unused synapses? Stevens focused her postdoctoral research on finding out. “We could have been completely wrong,” she recalls. “But we went for it.”

It paid off. In a 2007 paper, Barres and Stevens showed that C1q indeed plays a role in eliminating unneeded neurons in the developing brain. And they found that the protein is virtually absent in healthy adult neurons.

Now the scientists faced a new puzzle. Does C1q show up in brain diseases because the same mechanism involved in pruning a developing brain later goes awry? Indeed, evidence was already growing that one of the earliest events in neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s was significant loss of synapses.

When Stevens and Barres examined mice bred to develop glaucoma, a neurodegenerative disease that kills neurons in the optic system, they found that C1q appeared long before any other detectable sign that the disease was taking hold. It cropped up even before the cells started dying.

This suggested the immune cells might in fact cause the disease, or at the very least accelerate it. And that offered an intriguing possibility: that something could be made to halt the process. Barres founded a company, Annexon Biosciences, to develop drugs that could block C1q. Last week’s paper published by Barres, Stevens, and other researchers shows that a compound being tested by Annexon appears to be able to prevent the onset of Alzheimer’s in mice bred to develop the disease. Now the company hopes to test it in humans in the next two years.

Paths to treatments

To better understand the process that C1q helps trigger, Stevens and Barres wanted to figure out what actually plays the role of Pac-Man, eating up the synapses marked for death. It was well known that white blood cells known as macrophages gobbled up diseased cells and foreign invaders in the rest of the body. But macrophages are not usually present in the brain. For their theory to work, there had to be some other mechanism. And further research has shown that the cells doing the eating even in healthy brains are those mysterious clusters of material that Beth Stevens, for years, had been gazing right past in the microscope—the microglia that Río Hortega identified almost 100 years ago.

Now Stevens’s lab at Harvard, which she opened in 2008, devotes half its efforts to figuring out what microglia are doing and what causes them to do it. These cells, it turns out, appear in the mouse embryo at day eight, before any other brain cell, which suggests they might help guide the rest of brain development—and could contribute to any number of neurodevelopmental diseases when they go wrong.

Meanwhile, she is also expanding her study of the way different substances determine what happens in the brain. C1q is actually just the first in a series of proteins that accumulate on synapses marked for elimination. Stevens has begun to uncover evidence that there is a wide array of protective “don’t eat me” molecules too. It’s the balance between all these cues that regulates whether microglia are summoned to destroy synapses. Problems in any one could, conceivably, mess up the system.

Evidence is now growing that microglia are involved in several neurodevelopmental and psychiatric problems. The potential link to schizophrenia that was revealed in January emerged after researchers at the Broad Institute, led by Steven McCarroll and a graduate student named Aswin Sekar, followed a trail of genetic clues that led them directly to Stevens’s work. In 2009, three consortia from around the globe had published papers comparing DNA in people with and without schizophrenia. It was Sekar who identified a possible pattern: the more a specific type of protein was present in synapses, the higher the risk of developing the disease. The protein, C4, was closely related to C1q, the one first identified in the brain by Stevens and Barres.

McCarroll knew that schizophrenia strikes in late adolescence and early adulthood, a time when brain circuits in the prefrontal cortex undergo extensive pruning. Others had found that areas of the prefrontal cortex are among those most ravaged by the disease, which leads to massive synapse loss. Could it be that over-pruning by rogue microglia is part of what causes schizophrenia?

To find out, Sekar and McCarroll got in touch with Stevens, and the two labs began to hold joint weekly meetings. They soon demonstrated that C4 also had a role in pruning synapses in the brains of young mice, suggesting that excessive levels of the protein could indeed lead to over-pruning—and to the thinning out of brain tissue that appears to occur as symptoms such as psychotic episodes grow worse.

If the brain damage seen in Parkinson’s and Alzheimer’s stems from over-pruning that might begin early in life, why don’t symptoms of those diseases show up until later? Barres thinks he knows. He notes that the brain can normally compensate for injury by rewiring itself and generating new synapses. It also contains a lot of redundancy. That would explain why patients with Parkinson’s disease don’t show discernible symptoms until they have lost 90 percent of the neurons that produce dopamine.

It also might mean that subtle symptoms could in fact be detected much earlier. Barres points to a study of nuns published in 2000. When researchers analyzed essays the nuns had written upon entering their convents decades before, they found that women who went on to develop Alzheimer’s had shown less “idea density” even in their 20s. “I think the implication of that is they could be lifelong diseases,” Barres says. “The disease process could be going on for decades and the brain is just compensating, rewiring, making new synapses.” At some point, the microglia are triggered to remove too many cells, Barres argues, and the symptoms of the disease begin to manifest fully.

Turning this insight into a treatment is far from straightforward, because much remains unclear. Perhaps an overly aggressive response from microglia is determined by some combination of genetic variants not shared by everyone. Stevens also notes that diseases like schizophrenia are not caused by one mutation; rather, a wide array of mutations with small effects cause problems when they act in concert. The genes that control the production of C4 and other immune-system proteins may be only part of the story. That may explain why not everyone who has a C4 mutation will go on to develop schizophrenia.

Nonetheless, if Barres and Stevens are right that the immune system is a common mechanism behind devastating brain disorders, that in itself is a fundamental breakthrough. Because we have not known the mechanisms that trigger such diseases, medical researchers have been able only to alleviate the symptoms rather than attack the causes. There are no drugs available to halt or even slow neurodegeneration in diseases like Alzheimer’s. Some drugs elevate neurotransmitters in ways that briefly make it easier for individuals with dementia to form new synaptic connections, but they don’t reduce the rate at which existing synapses are destroyed. Similarly, there are no treatments that tackle the causes of autism or schizophrenia. Even slowing the progress of these disorders would be a major advance. We might finally go after diseases that have run unchecked for generations.

“We’re a ways away from a cure,” Stevens says. “But we definitely have a path forward.”

The complement protein C4 is a non-enzymatic component of the C3 and C5 convertases and thus essential for the propagation of the classical complement pathway. The covalent binding of C4 to immunoglobulins and immune complexes (IC) also enhances the solubilization of immune aggregates, and the clearance of IC through complement receptor one (CR1) on erythrocytes. Human C4 is the most polymorphic protein of the complement system. In this review, we summarize the current concepts on the 1-2-3 loci model of C4A and C4B genes in the population, factors affecting the expression levels of C4 transcripts and proteins, and the structural, functional and serological diversities of the C4A and C4B proteins. The diversities and polymorphisms of the mouse homologues Slp and C4 proteins are described and contrasted with their human homologues. The human C4 genes are located in the MHC class III region on chromosome 6. Each human C4 gene consists of 41 exons coding for a 5.4-kb transcript. The long gene is 20.6 kb and the short gene is 14.2 kb. In the Caucasian population 55% of the MHC haplotypes have the 2-locus, C4A-C4B configurations and 45% have an unequal number of C4A and C4B genes. Moreover, three-quarters of C4 genes harbor the 6.4 kb endogenous retrovirus HERV-K(C4) in the intron 9 of the long genes. Duplication of a C4 gene always concurs with its adjacent genes RP, CYP21 and TNX, which together form a genetic unit termed an RCCX module. Monomodular, bimodular and trimodular RCCX structures with 1, 2 and 3 complement C4 genes have frequencies of 17%, 69% and 14%, respectively. Partial deficiencies of C4A and C4B, primarily due to the presence of monomodular haplotypes and homo-expression of C4A proteins from bimodular structures, have a combined frequency of 31.6%. Multiple structural isoforms of each C4A and C4B allotype exist in the circulation because of the imperfect and incomplete proteolytic processing of the precursor protein to form the beta-alpha-gamma structures. Immunofixation experiments of C4A and C4B demonstrate > 41 allotypes in the two classes of proteins. A compilation of polymorphic sites from limited C4 sequences revealed the presence of 24 polymophic residues, mostly clustered C-terminal to the thioester bond within the C4d region of the alpha-chain. The covalent binding affinities of the thioester carbonyl group of C4A and C4B appear to be modulated by four isotypic residues at positions 1101, 1102, 1105 and 1106. Site directed mutagenesis experiments revealed that D1106 is responsible for the effective binding of C4A to form amide bonds with immune aggregates or protein antigens, and H1106 of C4B catalyzes the transacylation of the thioester carbonyl group to form ester bonds with carbohydrate antigens. The expression of C4 is inducible or enhanced by gamma-interferon. The liver is the main organ that synthesizes and secretes C4A and C4B to the circulation but there are many extra-hepatic sites producing moderate quantities of C4 for local defense. The plasma protein levels of C4A and C4B are mainly determined by the corresponding gene dosage. However, C4B proteins encoded by monomodular short genes may have relatively higher concentrations than those from long C4A genes. The 5′ regulatory sequence of a C4 gene contains a Spl site, three E-boxes but no TATA box. The sequences beyond–1524 nt may be completely different as the C4 genes at RCCX module I have RPI-specific sequences, while those at Modules II, III and IV have TNXA-specific sequences. The remarkable genetic diversity of human C4A and C4B probably promotes the exchange of genetic information to create and maintain the quantitative and qualitative variations of C4A and C4B proteins in the population, as driven by the selection pressure against a great variety of microbes. An undesirable accompanying byproduct of this phenomenon is the inherent deleterious recombinations among the RCCX constituents leading to autoimmune and genetic disorders.

C4A isotype is responsible for effective binding to form amide bonds with immune aggregates or protein antigens, while C4B isotype catalyzes the transacylation of the thioester carbonyl group to form ester bonds with carbohydrate antigens.

Derived from proteolytic degradation of complement C4, C4a anaphylatoxin is a mediator of local inflammatory process.

Compared to the brains of healthy individuals, those of people with schizophrenia have higher expression of a gene called C4, according to a paper published inNature today (January 27). The gene encodes an immune protein that moonlights in the brain as an eradicator of unwanted neural connections (synapses). The findings, which suggest increased synaptic pruning is a feature of the disease, are a direct extension of genome-wide association studies (GWASs) that pointed to the major histocompatibility (MHC) locus as a key region associated with schizophrenia risk.

“The MHC [locus] is the first and the strongest genetic association for schizophrenia, but many people have said this finding is not useful,” said psychiatric geneticist Patrick Sullivan of the University of North Carolina School of Medicine who was not involved in the study. “The value of [the present study is] to show that not only is it useful, but it opens up new and extremely interesting ideas about the biology and therapeutics of schizophrenia.”

Schizophrenia has a strong genetic component—it runs in families—yet, because of the complex nature of the condition, no specific genes or mutations have been identified. The pathological processes driving the disease remain a mystery.

Researchers have turned to GWASs in the hope of finding specific genetic variations associated with schizophrenia, but even these have not provided clear candidates.

“There are some instances where genome-wide association will literally hit one base [in the DNA],” explained Sullivan. While a 2014 schizophrenia GWAS highlighted the MHC locus on chromosome 6 as a strong risk area, the association spanned hundreds of possible genes and did not reveal specific nucleotide changes. In short, any hope of pinpointing the MHC association was going to be “really challenging,” said geneticist Steve McCarroll of Harvard who led the new study.

Nevertheless, McCarroll and colleagues zeroed in on the particular region of the MHC with the highest GWAS score—the C4 gene—and set about examining how the area’s structural architecture varied in patients and healthy people.

The C4 gene can exist in multiple copies (from one to four) on each copy of chromosome 6, and has four different forms: C4A-short, C4B-short, C4A-long, and C4B-long. The researchers first examined the “structural alleles” of the C4 locus—that is, the combinations and copy numbers of the different C4 forms—in healthy individuals. They then examined how these structural alleles related to expression of both C4Aand C4B messenger RNAs (mRNAs) in postmortem brain tissues.

Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia’s strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.

The strongest genetic association found in schizophrenia is its association to genetic markers across the major histocompatibility complex (MHC) locus, first described in three Nature papers in 2009. …

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission1. Recent studies implicate rare, large, high-penetrance copy number variants in some cases2, but the genes or biological mechanisms that underlie susceptibility are not known. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended major histocompatibility complex region on chromosome 6. We carried out a genome-wide association study of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium and SGENE data sets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10-9). This region includes a histone gene cluster and several immunity-related genes—possibly implicating aetiological mechanisms involving chromatin modification, transcriptional regulation, autoimmunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.

A genome-wide association study using the Molecular Genetics of Schizophrenia case-control data set, followed by a meta-analysis that included over 8,000 cases and 19,000 controls, revealed that while common genetic variation that underlies risk to schizophrenia can be identified, there probably are few or no single common loci with large effects. The common variants identified here lie on chromosome 6p22.1 in a region that includes a histone gene cluster and several genes implicated in immunity.

Schizophrenia is a complex disorder, caused by both genetic and environmental factors and their interactions. Research on pathogenesis has traditionally focused on neurotransmitter systems in the brain, particularly those involving dopamine. Schizophrenia has been considered a separate disease for over a century, but in the absence of clear biological markers, diagnosis has historically been based on signs and symptoms. A fundamental message emerging from genome-wide association studies of copy number variations (CNVs) associated with the disease is that its genetic basis does not necessarily conform to classical nosological disease boundaries. Certain CNVs confer not only high relative risk of schizophrenia but also of other psychiatric disorders1, 2, 3. The structural variations associated with schizophrenia can involve several genes and the phenotypic syndromes, or the ‘genomic disorders’, have not yet been characterized4. Single nucleotide polymorphism (SNP)-based genome-wide association studies with the potential to implicate individual genes in complex diseases may reveal underlying biological pathways. Here we combined SNP data from several large genome-wide scans and followed up the most significant association signals. We found significant association with several markers spanning the major histocompatibility complex (MHC) region on chromosome 6p21.3-22.1, a marker located upstream of the neurogranin gene (NRGN) on 11q24.2 and a marker in intron four of transcription factor 4 (TCF4) on 18q21.2. Our findings implicating the MHC region are consistent with an immune component to schizophrenia risk, whereas the association with NRGN and TCF4 points to perturbation of pathways involved in brain development, memory and cognition.

Letter

The International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature460, 748-752 (6 August 2009) | doi:10.1038/nature08185; Received 11 February 2009; Accepted 8 June 2009; Published online 1 July 2009; Corrected 6 August 2009

Schizophrenia is a severe mental disorder with a lifetime risk of about 1%, characterized by hallucinations, delusions and cognitive deficits, with heritability estimated at up to 80%1, 2. We performed a genome-wide association study of 3,322 European individuals with schizophrenia and 3,587 controls. Here we show, using two analytic approaches, the extent to which common genetic variation underlies the risk of schizophrenia. First, we implicate the major histocompatibility complex. Second, we provide molecular genetic evidence for a substantial polygenic component to the risk of schizophrenia involving thousands of common alleles of very small effect. We show that this component also contributes to the risk of bipolar disorder, but not to several non-psychiatric diseases.

Genome-wide association studies (GWAS) have yielded a plethora of new findings in the past 3 years. By early 2009, GWAS on 47 samples of subjects with attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder and schizophrenia will be completed. Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry (59 000 independent cases and controls, 7700 family trios and >40 billion genotypes). We know that GWAS can work, and the question now is whether it will work for psychiatric disorders. In this review, we describe these studies, the Psychiatric GWAS Consortium for meta-analyses of these data, and provide a logical framework for interpretation of some of the conceivable outcomes.

The purpose of this article is to consider the ‘big picture’ and to provide a logical framework for the possible outcomes of these studies. This is not a review of GWAS per se as many excellent reviews of this technically and statistically intricate methodological approach are available.7, 8, 9, 10, 11, 12 This is also not a review of the advantages and disadvantages of different study designs and sampling strategies for the dissection of complex psychiatric traits. We would like to consider how the dozens of GWAS papers that will soon be in the literature can be synthesized: what can integrated mega-analyses (meta-analysis is based on summary data (for example, odds ratios) from all available studies whereas ‘mega-analysis’ uses individual-level genotype and phenotype data) of all available GWAS data tell us about the etiology of these psychiatric disorders? This is an exceptional opportunity as positive or negative results will enable us to learn hard facts about these critically important psychiatric disorders. We suggest that it is not a matter of ‘success versus failure’ or ‘optimism versus pessimism’ but rather an opportunity for systematic and logical approaches to empirical data whereby both positive and appropriately qualified negative findings are informative.

The studies that comprise the Psychiatric GWAS Consortium (PGC; http://pgc.unc.edu) are shown in Table 1. GWAS data for ADHD, autism, bipolar disorder, major depressive disorder and schizophrenia from 42 samples of European subjects should be available for mega-analyses by early 2009 (>59 000 independent cases and controls and >7700 family trios). To our knowledge, the PGC will have access to the largest set of GWAS data available.

A major change in human genetics in the past 5 years has been in the growth of controlled-access data repositories, and individual phenotype and genotype data are now available for many of the studies in Table 1. When the PGC mega-analyses are completed, most data will be available to researchers via the NIMH Human Genetics Initiative (http://nimhgenetics.org). Although the ready availability of GWAS data is a benefit to the field by allowing rapid application of a wide range of analytic strategies to GWAS data, there are potential disadvantages. GWAS mega-analysis is complex and requires considerable care and expertise to be done validly. For psychiatric phenotypes, there is the additional challenge of working with disease entities based largely on clinical description, with unknown biological validity and having both substantial clinical variation within diagnostic categories as well as overlaps across categories.13 Given the urgent need to know if there are replicable genotype–phenotype associations, a new type of collaboration was required.

The purpose of the PGC is to conduct rigorous and comprehensive within- and cross-disorder GWAS mega-analyses. The PGC began in early 2007 with the principal investigators of the four GAIN GWAS,14 and within six months had grown to 110 participating scientists from 54 institutions in 11 countries. The PGC has a coordinating committee, five disease-working groups, a cross-disorder group, a statistical analysis and computational group, and a cluster computer for statistical analysis. It is remarkable that almost all investigators approached agreed to participate and that no one has left the PGC. Most effort is donated but we have obtained funding from the NIMH, the Netherlands Scientific Organization, Hersenstichting Nederland and NARSAD.

The PGC has two major specific aims. (1) Within-disorder mega-analyses: conduct separate mega-analyses of all available GWAS data for ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia to attempt to identify genetic variation convincingly associated with any one of these five disorders. (2) Cross-disorder mega-analyses: the clinically-derived DSM-IV and ICD-10 definitions may not directly reflect the fundamental genetic architecture.15 There are two subaims. (2a) Conduct mega-analysis to identify genetic variation convincingly associated with conventional definitions of two or more disorders. This nosological aim could assist in delineating the boundaries of this set of disorders. (2b) An expert working group will convert epidemiological and genetic epidemiological evidence into explicit hypotheses about overlap among these disorders, and then conduct mega-analyses based on these definitions (for example, to examine the lifetime presence of idiopathic psychotic features without regard to diagnostic context).

The goal of the PGC is to identify convincing genetic variation-disease associations. A convincing association would be extremely unlikely to result from chance, show consistent effect sizes across all or almost all samples and be impervious to vigorous attempts to disprove the finding (for example, by investigating sources of bias, confirmatory genotyping, and so on). Careful attention will be paid to the impact of potential sources of heterogeneity17 with the goal of assessing its impact without minimizing its presence.

Biological plausibility is not an initial requirement for a convincing statistical association, as there are many examples in human genetics of previously unsuspected candidate genes nonetheless showing highly compelling associations. For example, multiple SNPs in intron 1 of the FTO gene were associated with body mass index in 13 cohorts with 38 759 participants18 and yet ‘FTO’ does not appear in an exhaustive 116 page compilation of genetic studies of obesity.19 Some strong associations are in gene deserts: multiple studies have found convincing association between prostate cancer and a region on 8q24 that is ~250 kb from the nearest annotated gene.20 Both of these examples are being intensively investigated and we suspect that a compelling mechanistic ‘story’ will emerge in the near future. The presence of a compelling association without an obvious biological mechanism establishes a priority research area for molecular biology and neuroscience of a psychiatric disorder.

The PGC will use mega-analysis as the main analytic tool as individual-level data will be available from almost all samples. To wield this tool appropriately, a number of preconditions must be met. First, genotype data from different GWAS platforms must be made comparable as the direct overlap between platforms is often modest. This requires meticulous quality control for the inclusion of both SNPs and subjects and attention to the factors that can cause bias (for example, population stratification, cryptic relatedness or genotyping batch effects). Genotype harmonization can be accomplished using imputation (21, 22, for example) so that the same set of ~2 million23, 24 directly or imputed SNP genotypes are available for all subjects. Second, phenotypes need to be harmonized across studies. This is one of the most crucial components of the PGC and we are fortunate to have world experts directing the work. Third, the mega-analyses will assess potential heterogeneity of associations across samples.

A decision-tree schematic of the potential outcomes of the PGC mega-analyses is shown in Figure 1. Note that many of the possibilities in Figure 1 are not mutually exclusive and different disorders may take different paths through this framework. It is possible that there eventually will be dozens or hundreds of sequence variants strictly associated with these disorders with frequencies ranging from very rare to common.

………

GWAS has the potential to yield considerable insights but it is no panacea and may well perform differently for psychiatric disorders. Even if these psychiatric GWAS efforts are successful, the outcomes will be complex. GWAS may help us learn that clinical syndromes are actually many different things—for example, proportions of individuals with schizophrenia might evidence associations with rare CNVs of major effect,5, 6 with more common genetic variation in dozens (perhaps hundreds) of genomic regions, between genetic variation strongly modified by environmental risk factors, and some proportion may be genetically indistinguishable from the general population. Moreover, as fuel to long-standing ‘lumper versus splitter’ debates in psychiatric nosology, empirical data might show that some clinical disorders or identifiable subsets of subjects might overlap considerably.

The critical advantage of GWAS is the search of a ‘closed’ hypothesis space. If the large amount of GWAS data being generated are analyzed within a strict and coherent framework, it should be possible to establish hard facts about the fundamental genetic architecture of a set of important psychiatric disorders—which might include positive evidence of what these disorders are or exclusionary evidence of what they are not. Whatever the results, these historically large efforts should yield hard facts about ADHD, autism, bipolar disorder, major depressive disorder and schizophrenia that may help guide the next era of psychiatric research.

Reduced fecundity, associated with severe mental disorders1, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism2, schizophrenia3 and mental retardation4. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation4, 5 and autism2. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novoCNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.

The C4 gene can exist in multiple copies (from one to four) on each copy of chromosome 6, and has four different forms: C4A-short, C4B-short, C4A-long, and C4B-long. The researchers first examined the “structural alleles” of the C4 locus—that is, the combinations and copy numbers of the different C4 forms—in healthy individuals. They then examined how these structural alleles related to expression of both C4Aand C4B messenger RNAs (mRNAs) in postmortem brain tissues.

From this the researchers had a clear picture of how the architecture of the C4 locus affected expression ofC4A and C4B. Next, they compared DNA from roughly 30,000 schizophrenia patients with that from 35,000 healthy controls, and a correlation emerged: the alleles most strongly associated with schizophrenia were also those that were associated with the highest C4A expression. Measuring C4A mRNA levels in the brains of 35 schizophrenia patients and 70 controls then revealed that, on average, C4A levels in the patients’ brains were 1.4-fold higher.

C4 is an immune system “complement” factor—a small secreted protein that assists immune cells in the targeting and removal of pathogens. The discovery of C4’s association to schizophrenia, said McCarroll, “would have seemed random and puzzling if it wasn’t for work . . . showing that other complement components regulate brain wiring.” Indeed, complement protein C3 locates at synapses that are going to be eliminated in the brain, explained McCarroll, “and C4 was known to interact with C3 . . . so we thought well, actually, this might make sense.”

McCarroll’s team went on to perform studies in mice that revealed C4 is necessary for C3 to be deposited at synapses. They also showed that the more copies of the C4 gene present in a mouse, the more the animal’s neurons were pruned.

Synaptic pruning is a normal part of development and is thought to reflect the process of learning, where the brain strengthens some connections and eradicates others. Interestingly, the brains of deceased schizophrenia patients exhibit reduced neuron density. The new results, therefore, “make a lot of sense,” said Cardiff University’s Andrew Pocklington who did not participate in the work. They also make sense “in terms of the time period when synaptic pruning is occurring, which sort of overlaps with the period of onset for schizophrenia: around adolescence and early adulthood,” he added.

“[C4] has not been on anybody’s radar for having anything to do with schizophrenia, and now it is and there’s a whole bunch of really neat stuff that could happen,” said Sullivan. For one, he suggested, “this molecule could be something that is amenable to therapeutics.”

UniProtKB

Derived from proteolytic degradation of complement C4, C4a anaphylatoxin is a mediator of local inflammatory process. It induces the contraction of smooth muscle, increases vascular permeability and causes histamine release from mast cells and basophilic leukocytes.

Non-enzymatic component of C3 and C5 convertases and thus essential for the propagation of the classical complement pathway. Covalently binds to immunoglobulins and immune complexes and enhances the solubilization of immune aggregates and the clearance of IC through CR1 on erythrocytes. C4A isotype is responsible for effective binding to form amide bonds with immune aggregates or protein antigens, while C4B isotype catalyzes the transacylation of the thioester carbonyl group to form ester bonds with carbohydrate antigens.

Mapping the dreaming brain through neuroimaging and studies of brain damage

By Karen Zusi | March 1, 2016

Prefrontal leucotomies—surgeries to cut a section of white matter in the front of the brain, thus severing the frontal lobe’s connections to other brain regions—were all the rage through the 1950s as treatments for psychoses. The operations drastically altered the mental state of most patients. But along with personality changes, dulled initiative, and reduced imagination came a seemingly innocuous effect of many of these procedures: the patients stopped dreaming.

Mark Solms, a neuropsychologist at the University of Cape Town in South Africa, uncovered the correlation in historical data from around the globe as part of a long-term study to assess the impact, on dreams and dreaming, of damage to different parts of the brain. Between 1985 and 1995, Solms interviewed 332 of his own patients at hospitals in Johannesburg and London who had various types of brain trauma, asking them about their nightly experiences.

Solms identified two brain regions that appeared critical for the experience of dreaming. The first was at the junction of the parietal, temporal, and occipital lobes—a cortical area that supports spatial cognition and mental imagery. The second was the ventromesial quadrant of the frontal lobes, a lump of white matter commonly associated with goal-seeking behavior that links the limbic structures to the frontal cortex. “This lesion site rang a historical bell in my mind—that’s where the prefrontal leucotomy used to be done,” says Solms, adding that the operation controlled the hallucinations and delusions that came with psychosis. “That sort of struck me as, ‘Gosh, that’s what dreaming is.’” Lesions in other areas could intensify or reduce certain aspects of dreams, but damage to either of the regions Solms pinpointed reportedly caused dreaming to cease completely (Psychoanal Q, 64:43-67, 1995).

Advances in neuroimaging have lent more support to Solms’s brain map, and pinned down other areas that researchers now understand play a part in dream development. In 2013, Bill Domhoff, a psychologist from the University of California, Santa Cruz, and colleagues from the University of British Columbia published results that combined neuroimaging scans from separate studies of REM sleep and daydreaming. They discovered that brain regions that light up when there’s a high chance that one is dreaming overlapped with parts of the brain’s default mode network—regions active when the brain is awake but not focused on a specific external task (Front Hum Neurosci, 7:412, 2013). “It very much lines up,” says Domhoff. “It’s just stunning.”

The default mode network allows us to turn our attention inward, and dreaming is the extreme example, explains Jessica Andrews-Hanna, a cognitive scientist at the University of Colorado Boulder. The network takes up a large amount of cortical real estate. Key players are regions on the midline of the brain that support memories and future planning; these brain sections connect to other areas affecting how we process social encounters and imagine other individuals’ thoughts. “When people are sleeping—in particular, when they’re dreaming—the default mode network actually stays very active,” says Andrews-Hanna. With external stimuli largely cut off, the brain operates in a closed loop, and flights of fancy often ensue.

We usually take the bizarre nature of these experiences at face value. “Even in a completely crazy dream, we all think that it’s normal,” says Martin Dresler, a cognitive neuroscientist at Radboud University in the Netherlands. Dresler and many other researchers attribute this blasé acceptance to the deactivation of a brain region called the dorsolateral prefrontal cortex. When we sleep, the dorsolateral prefrontal cortex powers down, and higher executive control—which would normally flag a nonsensical concern, such as running late for a class when you haven’t been in school for a decade, as unimportant—evaporates. “You have this overactive default mode network with no connectivity, with no communication with regions that are important for making sense of the thoughts,” says Andrews-Hanna.

In healthy sleeping subjects, these executive functions can be unlocked in what’s known as lucid dreaming, when the prefrontal cortex reactivates and sleepers gain awareness of and control over their imagined actions. A lucid dreamer can actually “direct” a dream as it unfolds, deciding to fly, for example, or turning a nightmarish monster into a docile pet.

Records of lucid dreaming are limited to REM sleep, the sleep stage where the brain is most active. REM sleep normally induces paralysis to prevent people from acting out their dreams, but the eye muscles are exempt, and this gives skilled lucid dreamers a way to signal their lucidity to researchers.

Dresler’s team is using this phenomenon as a tool to ask specific questions about dreams. Before trained lucid dreamers fall asleep in Dresler’s lab, they agree to flick their eyes from left to right as soon as they realize within a dream that they’re asleep. The dreamed movement causes their actual eyes to move in a similar way under their closed eyelids. Researchers mark this signal as the beginning of a lucid dream, and then track brain patterns associated with specific dreamed actions. Dreaming also occurs in non-REM sleep, but with the brain less active, the eye muscles won’t respond to dream input—so there’s no robust way to tell if lucid dreaming takes place.

When subjects achieved lucidity and consciously dreamed that they performed a predetermined hand movement, Dresler’s research team observed activity in the sensorimotor cortex matching what would occur if the subjects actually moved their hands while awake (Curr Biol, 21:1833-37, 2011). “It’s probably the case that, for most of what we are dreaming about, the very same machinery and the very same brain regions are active compared to wakefulness,” says Dresler. “It’s just that the motor execution is stopped at the spinal level.”

Beyond sleep research, tracking lucid and normal dreaming offers an investigative model to study aspects of psychosis, according to some researchers. “These regions that are activated during lucid dreaming are typically impaired in patients with psychosis,” explains Dresler. “Having insight into your non-normal mental state in dreaming shares neural correlates with having insights into your non-normal state of consciousness in psychosis.” Dresler proposes training patients in early stages of psychosis to dream lucidly, in the hope that it might grant them some therapeutically relevant understanding of their illness.

While executive functions are impaired in many patients suffering from psychosis, their default networks seem to be overactive, says Andrews-Hanna. But how much similarity exists between the brain states of dreaming and psychosis remains controversial. Domhoff emphasizes the unique nature of dreams. “They’re not like schizophrenia, they’re not like meditation, they’re not like any kind of drug trip,” he says. “They’re an enactment of a scenario that is based upon various wishes and concerns.”

Ultimately, says Solms, deciphering dreaming furthers the field’s knowledge of what the brain does, as much as studies conducted during waking hours. “If you’re a clinician, and you understand what the different parts of the brain do in relation to dreaming, then it’s one of the things you can use as a road map for evaluating your patients.”

Since the discovery of the close association between rapid eye movement (REM) sleep and dreaming, much effort has been devoted to link physiological signatures of REM sleep to the contents of associated dreams [1, 2, 3 and 4]. Due to the impossibility of experimentally controlling spontaneous dream activity, however, a direct demonstration of dream contents by neuroimaging methods is lacking. By combining brain imaging with polysomnography and exploiting the state of “lucid dreaming,” we show here that a predefined motor task performed during dreaming elicits neuronal activation in the sensorimotor cortex. In lucid dreams, the subject is aware of the dreaming state and capable of performing predefined actions while all standard polysomnographic criteria of REM sleep are fulfilled [5 and 6]. Using eye signals as temporal markers, neural activity measured by functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) was related to dreamed hand movements during lucid REM sleep. Though preliminary, we provide first evidence that specific contents of REM-associated dreaming can be visualized by neuroimaging.

Highlights

► Eye signals can be used to access dream content with concurrent EEG and neuroimaging

Lucid dreaming is a rare but robust state of sleep that can be trained [5]. Phenomenologically, it comprises features of both waking and dreaming [7]: in lucid dreams, the sleeping subject becomes aware of his or her dreaming state, has full access to memory, and is able to volitionally control dreamed actions [6]. Although all standard polysomnographic criteria of rapid eye movement (REM) sleep [8] are maintained and REM sleep muscle atonia prevents overt motor behavior, lucid dreamers are able to communicate their state by predefined volitional eye movements [6], clearly discernable in the electrooculogram (EOG) (Figure 1). Combining the techniques of lucid dreaming, polysomnography, and brain imaging via functional magnetic resonance imaging (fMRI) or near-infrared spectroscopy (NIRS), we demonstrate the possibility to investigate the neural underpinnings of specific dream contents—in this case, dreamed hand clenching. Predecided eye movements served as temporal markers for the onset of hand clenching and for hand switching. Previous studies have shown that muscle atonia prevents the overt execution of dreamed hand movements, which are visible as minor muscle twitches at most [3 and 9].

Functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD)-response increases were contrasted between left and right hand movements (columns) in the three conditions (rows): executed hand movement during wakefulness (WE) (A), imagined hand movement during wakefulness (WI) (B), and dreamed hand movement during lucid REM sleep (LD) (C). Effects of left (right) hand movements were calculated in a fixed-effects analysis as a contrast “left > right” and “right > left,” respectively. Subpanels depict results in an SPM glass-brain view (sagital and coronal orientation) to demonstrate the regional specificity of the associated cortical activation, along with sensorimotor activation overlaid on an axial slice of the subject’s T1-weighted anatomical scan (position indicated on the glass brain for condition A). Clusters of activation in the glass-brain views are marked using the numbering given in Table S1. Red outlines in the glass-brain views mark the extent of activation found in the WE condition. This region of interest (ROI) was derived from the respective activation map during executed hand movement (A), thresholded at whole-brain corrected pFWE < 0.005, cluster extent >50 voxels, and served as a ROI for analysis of the WI and LD conditions in (B) and (C), respectively. T values are color-coded as indicated. The time course of the peak voxel inside the ROI is depicted (black) along with the predicted hemodynamic response based on the external pacing (A and B) or the predefined LRLR-eye signals during (C). The maximal difference in activation of the peak voxel between conditions is indicated as percentage of BOLD signal fluctuations of the predicted time course (gray).

FMRI results were confirmed by an independent imaging method in a second subject: NIRS data showed a typical hemodynamic response pattern of increased contralateral oxygenation over the sensorimotor region during successful task performance in lucid REM sleep (Figure 3; Figure 4). Notably, during dreaming, the hemodynamic responses were smaller in the sensorimotor cortex but of similar amplitude in the supplementary motor area (SMA) when compared to overt motor performance during wakefulness.

Figure 3.

Near-Infrared Spectroscopy Topography

Concentration changes of oxygenated (Δ[HbO], upper panel) and deoxygenated hemoglobin (Δ[HbR], lower panel) during executed (WE) and imagined (WI) hand clenching in the awake state and dreamed hand clenching (LD). The optical probe array covered an area of ∼7.5 × 12.5 cm2 over the right sensorimotor area. The solid box indicates the ROI over the right sensorimotor cortex with near-infrared spectroscopy (NIRS)-channels surrounding the C4-EEG electrode position. NIRS channels located centrally over midline and more anterior compared to sensorimotor ROI were chosen as ROI for the supplementary motor area (SMA, dotted box).

Figure 4.

Condition-Related NIRS Time Courses

Time courses of HbO (red traces) and HbR (blue traces) from the right sensorimotor ROI (left panel) and the supplementary motor ROI SMA (right panel) for executed (WE) and imagined (WI) hand clenching in the awake state and dreamed hand clenching (LD). The time courses represent averaged time courses from NIRS channels within the respective ROI (Figure 3). For each condition, 0 s denotes the onset of hand clenching indicated by LRLR-signals. Note that the temporal dynamics, i.e., an increase in HbO and a decrease in HbR, are in line with the typical hemodynamic response. Overt movement during wakefulness (dark red/blue traces) showed the strongest hemodynamic response, whereas the motor-task during dreaming leads to smaller changes (light red/blue traces). In the SMA, the hemodynamic response was stronger during the dreamed task when compared to imagery movement during wakefulness

Neurophysiological studies suggest that during REM sleep, the brain functions as a closed loop system, in which activation is triggered in pontine regions while sensory input is gated by enhanced thalamic inhibition and motor output is suppressed by atonia generated at the brain stem level [4 and 12].

Efforts have been made to correlate REMs to gaze direction during dreams—the “scanning hypothesis” [1 and 2]—and indeed similar cortical areas are involved in eye movement generation in wake and REM sleep [17]. In a similar vein, small muscle twitches during REM sleep were presumed to signal a change in the dream content [3]. Dream research methodology mostly relies on the evaluation of subjective reports of very diverse dream contents.

During dreaming, activation was much more localized in small clusters representing either generally weaker activation or focal activation of hand areas only, with signal fluctuations only in the order of 50% as compared to the actually executed task during wakefulness. The SMA is involved in timing, preparation, and monitoring of movements [21], and linked to the retrieval of a learned motor sequence especially in the absence of external cues [22]. Our NIRS data speak for an activation of SMA even during simple movements. This is in line with several PET and fMRI studies reporting SMA activations for simple tasks such as hand clenching, single finger-tapping, and alternated finger-tapping.

While You Were Sleeping

Assessing body position in addition to activity may improve monitoring of sleep-wake periods.

By Ruth Williams | March 1, 2016

Polysomnography—the combined assessment of brain waves, heart rate, oxygen saturation, muscle activity, and other parameters—is the most precise way to track a person’s sleeping patterns. However, the equipment required for such analyses is expensive, bulky, and disruptive to natural behavior.

Researchers are thus searching for ways to improve the accuracy of wearable devices while maintaining user-friendliness. Maria Angeles Rol of the University of Murcia in Spain and her colleagues have now discovered that by using a device strapped to the patient’s upper arm that measures both arm activity and position (the degree of tilt), they can more precisely detect periods of sleep.

The researchers studied just 13 people in this pilot study, says Barbara Galland of the University of Otago in New Zealand, but adds that nonetheless it “provide[s] an opening for further investigations to demonstrate the value of this novel technique.” (Chronobiol Int, 32:701-10, 2015)

Validation of an innovative method, based on tilt sensing, for the assessment of activity and body position

Since there is less movement during sleep than during wake, the recording of body movements by actigraphy has been used to indirectly evaluate the sleep–wake cycle. In general, most actigraphic devices are placed on the wrist and their measures are based on acceleration detection. Here, we propose an alternative way of measuring actigraphy at the level of the arm for joint evaluation of activity and body position. This method analyzes the tilt of three axes, scoring activity as the cumulative change of degrees per minute with respect to the previous sampling, and measuring arm tilt for the body position inference. In this study, subjects (N = 13) went about their daily routine for 7 days, kept daily sleep logs, wore three ambulatory monitoring devices and collected sequential saliva samples during evenings for the measurement of dim light melatonin onset (DLMO). These devices measured motor activity (arm activity, AA) and body position (P) using the tilt sensing of the arm, with acceleration (wrist acceleration, WA) and skin temperature at wrist level (WT). Cosinor, Fourier and non-parametric rhythmic analyses were performed for the different variables, and the results were compared by the ANOVA test. Linear correlations were also performed between actimetry methods (AA and WA) and WT. The AA and WA suitability for circadian phase prediction and for evaluating the sleep–wake cycle was assessed by comparison with the DLMO and sleep logs, respectively. All correlations between rhythmic parameters obtained from AA and WA were highly significant. Only parameters related to activity levels, such as mesor, RA (relative amplitude), VL5 and VM10 (value for the 5 and 10 consecutive hours of minimum and maximum activity, respectively) showed significant differences between AA and WA records. However, when a correlation analysis was performed on the phase markers acrophase, mid-time for the 10 consecutive hours of highest (M10) and mid-time for the five consecutive hours of lowest activity (L5) with DLMO, all of them showed a significant correlation for AA (R = 0.607, p = 0.028; R = 0.582, p = 0.037; R = 0.620, p = 0.031, respectively), while for WA, only acrophase did (R = 0.621, p = 0.031). Regarding sleep detection, WA showed higher specificity than AA (0.95 ± 0.01 versus 0.86 ± 0.02), while the agreement rate and sensitivity were higher for AA (0.76 ± 0.02 versus 0.66 ± 0.02 and 0.71 ± 0.03 versus 0.53 ± 0.03, respectively). Cohen’s kappa coefficient also presented the highest values for AA (0.49 ± 0.04) and AP (0.64 ± 0.04), followed by WT (0.45 ± 0.06) and WA (0.37 ± 0.04). The findings demonstrate that this alternative actigraphy method (AA), based on tilt sensing of the arm, can be used to reliably evaluate the activity and sleep–wake rhythm, since it presents a higher agreement rate and sensitivity for detecting sleep, at the same time allows the detection of body position and improves circadian phase assessment compared to the classical actigraphic method based on wrist acceleration.

Sleep’s Kernel

Surprisingly small sections of brain, and even neuronal and glial networks in a dish, display many electrical indicators of sleep.

Sleep is usually considered a whole-brain phenomenon in which neuronal regulatory circuits impose sleep on the brain. This paradigm has its origins in the historically important work of Viennese neurologist Constantin von Economo, who found that people who suffered from brain infections that damaged the anterior hypothalamus slept less. The finding was a turning point in sleep research, as it suggested that sleep was a consequence of active processes within the brain. This stood in stark contrast to the ideas of renowned St. Petersburg physiologist Ivan Pavlov, who believed that sleep resulted from the passive withdrawal of sensory input. Although the withdrawal of sensory input remains recognized as playing a role in sleep initiation, there is now much evidence supporting the idea that neuronal and glial activity in the anterior hypothalamus leads to the inhibition of multiple excitatory neuronal networks that project widely throughout the brain.

But we also know from millions of stroke cases that cause brain damage and from experimentally induced brain damage in animal models that, regardless of where a lesion occurs in the brain, including the anterior hypothalamus, all humans or animals that survive the brain damage will continue to sleep. Further, a key question remains inadequately answered: How does the hypothalamus know to initiate sleep? Unless one believes in the separation of mind and brain, then, one must ask: What is telling the hypothalamus to initiate sleep? If an answer is found, it leads to: What is telling the structure that told the hypothalamus? This is what philosophers call an infinite regress, an unacceptable spiral of logic.

For these reasons, 25 years ago the late Ferenc Obál Jr. of A. Szent-Györgyi Medical University in Szeged, Hungary, and I (J.K.) began questioning the prevailing ideas of how sleep is regulated. The field needed answers to fundamental questions. What is the minimum amount of brain tissue required for sleep to manifest? Where is sleep located? What actually sleeps? Without knowing what sleeps or where sleep is, how can one talk with any degree of precision about sleep regulation or sleep function? A new paradigm was needed.

There is no direct measure of sleep, and no single measure is always indicative of sleep. Quiescent behavior and muscle relaxation usually occur simultaneously with sleep but are also found in other circumstances, such as during meditation or watching a boring TV show. Sleep is thus defined in the clinic and in experimental animals using a combination of multiple parameters that typically correlate with sleep.

The primary tool for assessing sleep state in mammals and birds is the electroencephalogram (EEG). High-amplitude delta waves (0.5–4 Hz) are a defining characteristic of the deepest stage of non–rapid eye movement (non-REM) sleep. However, similar waves are evident in adolescents who hyperventilate for a few seconds while wide awake. Other measures used to characterize sleep include synchronization of electrical activity between EEG electrodes and the quantification of EEG delta wave amplitudes. Within specific sensory circuits, the cortical electrical responses induced by sensory stimulation (called evoked response potentials, or ERPs) are higher during sleep than during waking. And individual neurons in the cerebral cortex and thalamus display action potential burst-pause patterns of firing during sleep.

Using such measures, researchers have shown that different parts of the mammalian brain can sleep independently of one another. Well-characterized sleep regulatory substances, or somnogens, such as growth hormone releasing hormone (GHRH) and tumor necrosis factor α (TNF-α), can induce supranormal EEG delta waves during non-REM sleep in the specific half of the rat brain where the molecules were injected. Conversely, if endogenous TNF-α or GHRH production is inhibited, spontaneous EEG delta waves during non-REM sleep are lower on the side receiving the inhibitor. A more natural example of sleep lateralization is found in the normal unihemispheric sleep of some marine mammals. (See “Who Sleeps?”)

Much smaller parts of the brain also exhibit sleep-like cycles. As early as 1949, Kristian Kristiansen and Guy Courtois at McGill University and the Montreal Neurological Institute showed that, when neurons carrying input from the thalamus and surrounding cortical tissue are surgically severed, clusters of neurons called cerebral cortical islands will alternate between periods of high-amplitude slow waves that characterize sleep and low-amplitude fast waves typical of waking, independently of surrounding tissue.1 This suggests that sleep is self-organizing within small brain units.

In 1997, Ivan Pigarev of the Russian Academy of Sciences in Moscow and colleagues provided more-concrete evidence that sleep is a property of local networks. Measuring the firing patterns of neurons in monkeys’ visual cortices as the animals fell asleep while performing a visual task, they found that some of the neurons began to stop firing even while performance persisted. Specifically, the researchers found that, within the visual receptive field being engaged, cells on the outer edges of the field stopped firing first. Then, as the animal progressed deeper into a sleep state, cells in more-central areas stopped firing. This characteristic spatial distribution of the firing failures is likely a consequence of network behavior. The researchers thus concluded that sleep is a property of small networks.2

More recently, David Rector at Washington State University and colleagues provided support for the idea of locally occurring sleep-like states. In a series of experiments, they recorded electrical activity from single cortical columns using a small array of 60 electrodes placed over the rat somatosensory cortex. The sensory input from individual facial whiskers maps onto individual cortical columns. As expected, ERPs in the cortical columns induced by twitching a whisker were higher during sleep than during waking. But looking at the activity of individual columns, the researchers observed that they could behave somewhat independently of each other. When a rat slept, most—but not all—of the columns exhibited the sleep-like high-amplitude ERPs; during waking, most—but not all—of the columns were in a wake-like state. Interestingly, the individual cortical columns also exhibited patterns that resembled a sleep rebound response: the longer a column was in the wake-like state, the higher the probability that it would soon transition into a sleep-like state.3

To test how cortical-column state can affect whole-animal behavior, Rector and his team trained rats to lick a sucrose solution upon the stimulation of a single whisker, then characterized the whisker’s cortical-column state. If the column receiving input from the stimulated whisker was in a wake-like state (low-magnitude ERP), the rats did not make mistakes. But if the column was in the sleep-like state (high-magnitude ERP), the animals would fail to lick the sucrose when stimulated and would sometimes lick it even when their whisker was not flicked.4 Even though the animal was awake, if a cortical column receiving stimulation was asleep, it compromised the animal’s performance. These experiments indicate that even very small neuronal networks sleep and that the performance of learned behavior can depend on the state of such networks.

Given that sleep can manifest in relatively small brain regions, perhaps it should not be too surprising that co-cultures of neurons and glia possess many of the electrophysiological sleep phenotypes that are used to define sleep in intact animal brains. During sleep, cortical and thalamic neurons display bursts of action potentials lasting about 500 ms, followed by periods of hyperpolarization lasting about the same length of time. The synchronization of this firing pattern across many neurons is thought to generate the EEG activity characteristic of delta-wave sleep, and undisturbed co-cultures of glia and neurons display periodic bursts of action potentials, suggesting that the culture default state is sleep-like. In contrast, if neuronal and glia networks are stimulated with excitatory neurotransmitters, the culture’s “burstiness”—the fraction of all action potentials found within bursts—is reduced, indicating a transition to a wake-like state. Treatment of co-cultures with excitatory neurotransmitters also converts their gene expression profile from a spontaneous sleep-like pattern to a wake-like pattern.5

Cell cultures also respond to sleep-inducing agents similarly to whole organisms. If a neuronal and glial culture is treated with TNF-α, the synchronization and amplitudes of slow-wave electrical activity increase, indicating a deeper sleep-like state. Moreover, ERPs are of greater magnitude after cultures are treated with TNF-α than during the sleep-like default state, suggesting that the somnogen induces a deeper sleep-like state in vitro as it does in vivo.6

Researchers have even studied the developmental pattern of such sleep phenotypes, using multielectrode arrays to characterize network activity throughout the culture, and the emergence of network properties follows a similar time course as in intact mouse pups. Spontaneous action potentials occur during the first few days in culture, but network emergent properties are not evident until after about 10 days. Then, synchronization of electrical potentials begins to emerge, and the network’s slow waves begin to increase in amplitude. If the cultures are electrically stimulated, slow-wave synchronization and amplitudes are reduced, suggesting the networks wake up. This is followed by rebound-enhanced slow-wave synchronization and amplitudes the next day, suggesting sleep homeostasis is also a characteristic of cultured networks.6

Clearly, even small neural networks can exhibit sleep-like behavior, in a dish or in the brain. But the question remains: What is driving the oscillations between sleep- and wake-like states?

Sleep emerges

In the intact brain, communication among neurons and between neurons and other cells is ever changing. Bursts of action potentials trigger the release of multiple substances and changes in gene expression, both of which alter the efficacy of signal transmission. For instance, neural or glial activity induces the release of ATP into the local extracellular space. Extracellular ATP, in turn, induces changes in the expression of TNF-α and other somnogens known to induce a sleep-like state. Because these effects take place in the immediate vicinity of the cell activity, they target sleep to local areas that were active during prior wakefulness.

In 1993, Obál and I (J.K.) proposed that sleep is initiated within local networks as a function of prior activity.7 The following year, Derk-Jan Dijk and Alex Borbely of the University of Zurich provided support for this idea when they had volunteers hold hand vibrators in one hand during waking to stimulate one side of the somatosensory cortex. In subsequent sleep, the side of the brain that received input from the stimulated hand exhibited greater sleep intensity, determined from amplitudes of EEG slow waves, than the opposite side of the brain. And in 2006, Reto Huber, then at the University of Wisconsin, showed that if an arm is immobilized during waking, amplitudes of EEG slow waves from the side of the brain receiving input from that arm are lower in subsequent sleep.

These experiments indicate that local sleep depth is a function of the activity of the local network during waking—an idea that has been confirmed by multiple human and animal studies. Moreover, local network state oscillations strongly indicate that sleep is initiated within local networks such as cortical columns. But how do the states of a population of small networks translate into whole-animal sleep?

Small local clusters of neurons and glia are loosely connected with each other via electrophysiological and biochemical signaling, allowing for constant communication between local networks. Steven Strogatz of Cornell University showed that dynamically coupled entities, including small neuronal circuits, will synchronize with each other spontaneously without requiring direction by an external actor. Synchronization of loosely coupled entities occurs at multiple levels of complexity in nature from intact animals to molecules—for example, birds flocking, or the transition from water to ice. The patterns generated by bird flocking, or the hardness of ice, are called emergent properties.

We, Obál, and our colleagues proposed that whole-brain sleep is an emergent property resulting from the synchronization of local neuronal network states.7,8,9 This would explain why sleep continues to occur after brain damage: because the remaining local circuits will spontaneously synchronize with each other. This view also allows one to easily envision variations in the depth or degree of sleep and waking because it allows for some parts of the brain to be in sleep-like states while other areas are in wake-like states, just as Rector observed. These independent states of local networks may account for sleep inertia, the minutes-long period upon awakening of poor cognitive performance and fuzzy-mindedness, and may also play a role in the manifestation of dissociated states such as sleepwalking. Most importantly, this paradigm frees sleep regulation from the dualism trap of mind/brain separation: top-down imposition of state is not required for the initiation of local state oscillations or for subsequent whole-organism sleep to ensue.

Our theory is also consistent with the modulation of sleep and wakefulness by sleep regulatory circuits such as those in the hypothalamus. For example, if interleukin-1, a sleep regulatory substance, is applied locally to the surface of the rat cortex, it induces local high-amplitude EEG slow waves indicative of a greater local depth of sleep.10 The responses induced by interleukin-1 in the cortex enhanced neuronal activity in anterior hypothalamic sleep regulatory areas.11 That hypothalamic neuronal activity likely provides information on local sleep- and wake-like states occurring in the cortex to the hypothalamus, where it can modulate the orchestration of the sleep initiated within the smaller brain units.

Finally, our ideas may inform the study of how sleep influences the formation of memories. A fundamental problem a living brain faces is the incorporation of new memories and behaviors while conserving existing ones. We know that cell activity enhances neuronal connectivity and the efficacy of neurotransmission within active circuits, a phenomenon that has been posited to be a mechanism by which memories are formed and solidified. By themselves, however, these use-dependent mechanisms would lead to unchecked growth of connectivity (in response to activity patterns) and positive feedback (since increased connectivity leads to reuse), ultimately resulting in a rigid, non-plastic network.7 Instead, we suggest that biochemical mechanisms—specifically, the use-dependent expression of genes involved in sleep regulation and memory—induce oscillations, representing local wake- and sleep-like states, which serve to stabilize and preserve brain plasiticity.7

For more than a century, researchers have struggled to understand how sleep works and what it does. Perhaps this lack of answers stems from a fundamental misconception about what sleeps. By thinking about sleep in smaller units, such as individual networks in the brain, hopefully the field will start to understand what exactly is going on during this enigmatic—but very common—phenomenon.

James M. Krueger is a regents professor of neuroscience and Sandip Roy is an associate professor of electrical engineering at Washington State University.

In November 1986, Emmanuel Mignot arrived at Stanford University’s Center for Sleep Sciences and Medicine for a 16-month stint as a research associate. His goal was to find effective drugs to treat narcolepsy; his study subjects belonged to a colony of canines that suffered from the malady. “[When I got there], the dogs were being maintained, but not much was being done with them other than some chemistry studies on known neurotransmitters,” says Mignot, a professor of psychiatry and behavioral sciences at Stanford University and now director of the center. “As a pharmacologist, I wanted to study potential treatments for narcolepsy and understand the molecular biology to improve treatment in humans.”

The first narcoleptic dog, a French poodle named Monique, was brought to Stanford in 1974 byWilliam Dement, the so-called “father of sleep medicine,” who had founded the center in 1970, the first in the world dedicated to the study of sleep. Dement and other researchers there established a full breeding colony in 1977 when dogs with a genetic form of the neurological disorder were discovered—initially, some puppies from a litter of Dobermans and, later, some Labradors. Narcoleptic dogs and humans both exhibit a combination of symptoms: perpetual sleepiness, cataplexy—muscle paralysis attacks triggered by emotions—and abnormal rapid eye movement (REM) sleep. While the condition in humans and dogs is treatable, there is no cure.

To study which narcolepsy drugs increased wakefulness and decreased cataplexy in the dogs, Mignot and psychiatry professor Seiji Nishino used a food-elicited cataplexy test: administration of the drug followed by release into a room with pieces of food on the floor and careful observation. “The dog would rush into the room and be so happy to eat the treats, and then would have an attack and collapse on the floor.” The researchers counted the number and duration of the attacks after treatment with a drug at various doses. In humans, cataplexy episodes are triggered by a positive emotion such as laughter at a joke or pleasant surprise. “For the dogs, it is food or the joy of playing. That is what is great about dogs as a model for this condition. When you give a treatment to a rat or mouse and they stop having cataplexy, you really don’t know if it is because they don’t feel good or if it is a genuine effect. But the dogs show you emotions like humans. I knew all of these dogs by name. They were my friends. I could see if they were worried or didn’t feel well.”

Mignot worked mostly with the Dobermans and Labs, but there were also dogs donated to the colony that seemed to have a sporadic form of narcolepsy, “There was Vern, a miniature poodle; Wally, a big poodle; Tucker, a mutt; and Beau, my beloved dachshund.” Using the cataplexy test in animals along with in vitro studies of the drugs’ chemical properties, Mignot and Nishino found that antidepressants suppress cataplexy by inhibiting adrenergic reuptake, and that amphetamine-like stimulants promote wakefulness in narcoleptics by increasing the availability of dopamine. “We improved the then-current treatments and started to understand the kinds of chemicals important to regulate narcolepsy symptoms.”

But Mignot wanted to understand the molecular mechanism of narcolepsy, so he turned his focus to the genetic basis of the disorder. A lack of genetics training and no map of the dog genome to guide him did not deter Mignot. He has tirelessly pursued this previously little-studied and, so far, only known neurological disorder that fundamentally perturbs the nature of sleep states.

Here, Mignot talks about pursuing a master’s, PhD, and MD simultaneously, the paper retraction that has been the most difficult episode in his career so far, and his unexpected devotion to a Chihuahua.

Mignot Motivated

Sir Mix-a-Lot. The youngest of six siblings, Mignot had a penchant for collecting fossils and for conducting chemistry experiments in the bathroom of his family’s home in Paris. “I bought chemicals sold by a Chinese shopkeeper on Rue Saint-Dominique to do all kinds of experiments, mixed them, and occasionally made mistakes. There were burn marks and projections on the walls of my bathroom.” In high school, the self-proclaimed “nerd with glasses” became interested in biology, and, after graduation in 1977, went to study for a medical degree at the René Descartes University Faculty of Medicine in Paris.

Collecting degrees. “In the second year of medical school, I got bored from all of the memorization.” He took the entrance exam for the prestigious École Normale Supérieure (ENS), which gives students freedom to pursue their academic interests at other institutions while providing a stipend, housing, and the support of professor mentors. He passed, and entered the ENS in 1979. Mignot worked towards a master’s in biochemistry, and then a PhD in molecular pharmacology while still continuing his medical studies. “Nothing was set up for MD-PhD programs at the time. It was all in parallel, which was crazy. I had an exam every few weeks,” says Mignot. In 1984, he received both his medical degree and, later, a PhD from Pierre and Marie Curie University.

New to narcolepsy. Mignot became interested in the effects of drugs on the brains of psychiatric patients, studying how different compounds affected the metabolism of neurotransmitters in the brains of rats, and pursued a residency in psychiatry to complement his laboratory research. In 1986, he was offered a professorship in pharmacology at the Paris V University School of Medicine. But first, Mignot needed to complete the mandatory military service that he had deferred. “Instead of going to a former French colony to practice medicine, I convinced the French government to send me to Stanford to study modafinil, a wakefulness-promoting drug created by a French pharmaceutical company called Lafon Laboratories for the treatment of narcolepsy. I had never heard about [narcolepsy] during medical school—it must have been a single line in my textbooks. I discovered that Stanford was doing work on sleep and that Dement had started a colony of narcoleptic dogs there. I thought I could study these animals and figure out how modafinil worked.”

So Mignot came to Stanford for 16 months as part of his military service with financial support from Lafon Laboratories. “The company had claimed modafinil worked by a novel mechanism, unrelated to how stimulants work,” says Mignot. But Mignot found that modafinil bound the dopamine transporter, inhibiting the reuptake of the neurotransmitter, boosting wakefulness. “This is a similar mode of action as Ritalin, but the company was claiming otherwise. It took 10 years for my results to be validated, finally, by Nora D. Volkow, now director of the National Institute on Drug Abuse, who showed . . . that indeed the drug displaces the dopamine transporter at doses that increase wakefulness in humans.”

Mignot Moves

Going to the dogs. At Stanford, Mignot immersed himself in his work with the dog colony. “I worked all the time and came home just to sleep. I was definitely not very successful with girls then, because I smelled like dog all the time. I spent all day with the dogs, going to the facility, hugging, playing, and working with them. When we bred them, sometimes the mothers rejected their puppies so we had to come in every few hours, even in the middle of the night, to bottle-feed the puppies. Even after I took a shower, you could still smell the dogs. It was a strange part of my life.”

From pharmacology to genetics. Mignot kept extending his stay at Stanford. “After a few years I realized our pharmacology studies were never going to lead to narcolepsy’s cause. We needed to find the genetic cause in the dog.” In 1988, he resigned a faculty position in Paris—which was being held for him even as he continued to extend his time at Stanford—deciding to search for the mutated gene responsible for narcolepsy in dogs. In 1993, Mignot became the head of the Center for Narcolepsy at Stanford. A connection between an immune gene, the human leukocyte antigen (HLA) allele HLA-DR2, and narcolepsy in humans had already been identified by Yutaka Honda at the University of Tokyo, so Mignot’s lab tried to ascertain whether the same connection was true in the dogs or if the immune gene was simply a genetic linkage marker. These were the days before the dog or human genome had been sequenced, so the work took Mignot’s lab 10 years, and almost 200 narcoleptic Dobermans and Labradors: years of painstaking chromosome walking experiments, DNA fingerprinting, and the construction of a bacterial artificial chromosome library of dog genomic pieces. “What helped us a lot was that we knew the Dobermans and Labs had the same genetic defect because we interbred and got narcoleptic puppies—what’s called a complementation test.” In 1999, Mignot’s team identified the mutated gene as hypocretin receptor 2, whose protein binds hypocretin (also called orexin), a neuropeptide that regulates arousal and wakefulness. Several weeks later, after seeing these findings, Masashi Yanagisawa’s lab independently published a confirmation, showing that hypocretin knockout mice also have narcolepsy.

In parallel narcolepsy studies across ethnic groups, Mignot’s lab found that it was not the initial HLA-DR2allele that predisposed humans to narcolepsy, but another, nearby HLA gene, DQB1*0602.

Humans are not like dogs. “After we found the gene, the research went fast. We decided to look at hypocretin itself and see if it’s abnormal in humans.” Mignot’s lab sequenced the genes for the hypocretin receptor and its ligand in narcoleptic patients, expecting mutations in either to be rare because of the known HLA-narcolepsy linkage and the fact that most cases in humans, unlike in dogs, are not familial. Only one documented case, a child who had narcolepsy onset at six months of age, has been found to harbor a hypocretin gene mutation. “I think you need to knock out both receptor 1 and 2 in humans to get the full narcoleptic phenotype,” says Mignot. “Those with just one mutation may be more prone to tiredness but not full narcolepsy.”

In 2000, Mignot’s and Nishino’s groups reported that hypocretin was not present in narcoleptic patients’ cerebrospinal fluid—a test still used diagnostically today. The same year, independent studies from Mignot’s laboratory and that of Jerome Siegel at the University of California, Los Angeles, found that the lack of hypocretin was not due to gene mutations but to the fact hypocretin cells were missing in the brains of narcoleptic patients. HLA genes were well known to be associated with many autoimmune diseases, and Mignot hypothesized that hypocretin was missing due to an autoimmune attack against hypocretin-secreting neurons. What the abnormality is in those narcolepsy patients with normal hypocretin levels remains a mystery.

Mignot Moves Forward

Still a missing link. “I have been working on this [autoimmunity] hypothesis for 10 years, and we see that this hypothesis is more and more likely, but we cannot find any direct proof. It’s frustrating, but that kind of struggle is the story of my life.” All known autoimmune diseases result in the generation of antibodies in patients, but antibodies against hypocretin or the hypocretin cells have never been detected. So Mignot’s lab tested whether T-cells were the immune component attacking hypocretin. In 2013, his lab published a study identifying the T-cell culprits. But the study was retracted by Mignot himself one year later, when Mignot’s group couldn’t reproduce the results after the scientist who did most of the experiments had left the lab. “It was really painful and the worst time in my career.”

A new lead. “In 2010, a lot of people suddenly started to develop narcolepsy after receiving the Pandemrix vaccine against swine flu. It’s very odd. We still don’t understand why this particular vaccine increased the risk of narcolepsy.” Mignot thinks that a component of the vaccine or the virus itself triggers the immune system to attack hypocretin-producing neurons. “So now I am doing a lot of studies comparing the different vaccines and the wild-type virus to try to understand what could be common to produce this response. I think the vaccine will give us a final clue to isolate the immune T-cells involved in narcolepsy.”

Genetics of sleep. Mignot’s lab is working on a genome-wide association study, which shows that the genetic variants linked to narcolepsy are mostly immune-related, similar to Type 1 diabetes, celiac disease and other autoimmune diseases, further supporting the autoimmune hypothesis. Mignot is also getting a large human study off the ground. “I want to study the genetics of 40,000 people with sleep issues to see if there are genetic traits that cause people to sleep well or not sleep well, to need more sleep or less sleep. This hasn’t been done yet. I think this will help us crack open the mysteries of sleep.”

A new companion. “The dog colony was officially dismantled in 2000 after we found the canine narcolepsy gene. The dogs were adopted and we got Bear, a narcoleptic Schipperke. He passed away over a year ago. I loved that dog and miss him a lot. He was an unusually kind soul. Three months later, a breeder from Vermont called and said he had a narcoleptic Chihuahua. I flew to Vermont and adopted Watson and he’s been with us ever since. I never would have thought to adopt a Chihuahua, but now I can’t think of life without Watson. He is faithful and cuddly. I really think you can bond with any dog.”

The journey continues. “This story of narcolepsy, it’s a difficult story. Finding the gene was very difficult, and finding the autoimmune connection should have been trivial, but it has been an ordeal because there is absolutely no collateral damage. As [Stanford neurologist] Larry Steinman said to me, it’s like a ‘hit and run’—it looks like it was cleaned up and the players disappear. It’s hard, but by learning about this disease, we may discover other diseases where a similar autoimmune destruction happens in the brain but we have never realized it. I wouldn’t be surprised if some forms of depression and schizophrenia have an autoimmune basis in the brain. By experience, the more difficult it is, the most interesting the answer will be.”

Greatest Hits

Identified the gene for hypocretin receptor 2, which, when mutated, causes an inherited form of narcolepsy in Dobermans and Labradors

Identified how antidepressant and stimulant drugs work as treatments for narcolepsy

Identified DQB1*0602 as the main human gene associated with narcolepsy

By genome-wide association, found immune polymorphisms, such as one in the T-cell receptor alpha, that also predispose people to the disease, further suggesting the disease is autoimmune

Found that human narcolepsy, unlike canine narcolepsy, is not caused by mutations in the hypocretin receptor 2 gene but is due to an immune-mediated destruction of hypocretin-producing neurons in the brain

DQB1*0602 and DQA1*0102 (DQ1) are better markers than DR2 for narcolepsy in Caucasian and black Americans.

In the present study, we tested 19 Caucasian and 28 Black American narcoleptics for the presence of the human leucocyte antigen (HLA) DQB1*0602 and DQA1*0102 (DQ1) genes using a specific polymerase chain reaction (PCR)-oligotyping technique. A similar technique was also used to identify DRB1*1501 and DRB1*1503 (DR2). Results indicate that all but one Caucasian patient (previously identified) were DRB1*1501 (DR2) and DQB1*0602/DQA1*102 (DQ1) positive. In Black Americans, however, DRB1*1501 (DR2) was a poor marker for narcolepsy. Only 75% of patients were DR2 positive, most of them being DRB1*1503, but not DRB1*1501 positive. DQB1*0602 was found in all but one Black narcoleptic patient. The clinical and polygraphic results for this patient were typical, thus confirming the existence of a rare, but genuine form of DQB1*0602 negative narcolepsy. These results demonstrate that DQB1*0602/DQA1*0102 is the best marker for narcolepsy across all ethnic groups.

Narcolepsy is a chronic neurologic disorder characterized by excessive daytime sleepiness and abnormal manifestations of REM sleep including cataplexy, sleep paralysis, and hypnagogic hallucinations. Narcolepsy is both a significant medical problem and a unique disease model for the study of sleep. Research in human narcolepsy has led to the identification of specific HLA alleles (DQB1*0602 and DQA1*0102) that predispose to the disorder. This has suggested the possibility that narcolepsy may be an autoimmune disorder, a hypothesis that has not been confirmed to date. Genetic factors other than HLA are also likely to be involved. In a canine model of narcolepsy, the disorder is transmitted as a non-MHC single autosomal recessive trait with full penetrance (canarc-1). A tightly linked marker for canarc-1 has been identified, and positional cloning studies are under way to isolate canarc-1 from a newly developed canine genomic BAC library. The molecular cloning of this gene may lead to a better understanding of sleep mechanisms, as has been the case for circadian rhythms following the cloning of frq, per, and Clock.

Sleep consumes almost one-third of any human lifetime, yet its biological function remains unknown. Electrophysiological studies have shown that sleep is physiologically heterogeneous. Sleep onset is first characterized by light nonrapid eye movement (NREM) sleep (stage I and II), followed by deep NREM sleep or slow-wave sleep (stage III and IV) and finally rapid eye movement (REM) sleep. This sleep cycle is ∼90 min long and is repeated multiple times during nocturnal sleep. REM sleep, also called paradoxical sleep, is characterized by low-voltage fast electroencephalogram activity, increased brain metabolism, skeletal muscle atonia, rapid eye movements, and dreaming. Total sleep deprivation and/or REM sleep deprivation are both lethal in animals.

NREM and REM sleep are mainly regulated by circadian and homeostatic processes. Recent studies have suggested that across the animal kingdom, circadian rhythms are regulated by similar negative feedback loops involving the rhythmic expression of RNAs encoding proteins that act to shut off the genes encoding them (Hall 1995; Dunlap 1996;Rosbash et al. 1996; Young et al. 1996). From a genetic perspective, much less progress has been made in the noncircadian aspects of sleep regulation. This review demonstrates that a genetic approach to narcolepsy will in time provide a novel insight into the molecular basis of sleep control.

Narcolepsy, a Disorder of REM Sleep Regulation

Narcolepsy most often begins in the second decade of life but may be observed at the age of 5 or younger (Honda 1988). The cardinal symptom in narcolepsy is a persistent and disabling excessive daytime sleepiness. Sleep attacks are unpredictable, irresistible, and may lead to continuing activities in a semiconscious manner, a phenomenon referred to as automatic behavior. Naps are usually refreshing, but the restorative effect vanishes quickly.

Sleepiness is not sufficient to diagnose the disorder. Narcoleptic patients also experience symptoms that are secondary to abnormal transitions to REM sleep (Aldrich 1992; Bassetti and Aldrich 1996). The most important of these symptoms is cataplexy, a pathognomonic symptom for the disorder. In cataplexy, humor, laughter, or anger triggers sudden episodes of muscle weakness ranging from sagging of the jaw, slurred speech, buckling of the knees or transient head dropping, to total collapse to the floor (Aldrich 1992; Bassetti and Aldrich 1996). Patients typically remain conscious during the attack, which may last a few seconds or a few minutes. Reflexes are abolished during the attack, as they are during natural REM sleep atonia. Sleep paralysis, another manifestation of REM sleep atonia, is characterized by an inability to move and speak while falling asleep or upon awakening. Episodes last a few seconds to several minutes and can be very frightening. Hypnagogic hallucinations are vivid perceptual dream-like experiences (generally visual) occurring at sleep onset. Sleep paralysis and hypnagogic hallucinations occasionally occur in normal individuals under extreme circumstances of sleep deprivation or after a change in sleep schedule (Aldrich 1992; Bassetti and Aldrich 1996) and thus have little diagnostic value in isolation.

Nocturnal sleep polysomnography is conducted to exclude other possible causes of daytime sleepiness such as sleep apnea or periodic limb movements (Aldrich 1992). The Multiple Sleep Latency Test (MSLT) is also carried out to demonstrate daytime sleepiness objectively. In this test, patients are requested to take four or five naps at 2-hr intervals, during which time to sleep onset (sleep latency) is measured. Short sleep latencies under 5 min are usually observed in narcoleptic patients, together with abnormal REM sleep episodes, referred to as sleep-onset REM periods (SOREMPs). The combination of a history of cataplexy, short sleep latencies, and two or more SOREMPs during MSLT is diagnostic for narcolepsy (Bassetti and Aldrich 1996;Mignot 1996). Note that many naps consist only of NREM sleep suggesting that there is also a broader problem of impaired sleep–wake regulation, with indistinct boundaries between sleep and wakefulness in narcolepsy (Broughton et al. 1986; Bassetti and Aldrich 1996).

The disorder has a large psychosocial impact. Two-thirds of patients have fallen asleep while driving, and 78% suffer from reduced performance at work (Broughton et al. 1981). Depression occurs in up to 23% of cases (Roth 1980). Treatment is purely symptomatic and generally involves amphetamine-like stimulants for excessive daytime sleepiness and antidepressive treatment for cataplexy and other symptoms of abnormal REM sleep (Bassetti and Aldrich 1996; Nishino and Mignot 1997).

Familial and Genetic Aspects of Human Narcolepsy

Narcolepsy–cataplexy affects 0.02%–0.18% of the general population in various ethnic groups (Mignot 1998). A familial tendency for narcolepsy has long been recognized (Roth 1980). The familial risk of a first-degree relative is 0.9%–2.3% for narcolepsy–cataplexy, which is 10–40 times higher than the prevalence in the general population (Mignot 1998).

In a Finnish twin cohort study consisting of 13,888 monozygotic (MZ) and same-sexed dizygotic (DZ) twin pairs, three narcoleptic individuals were found and each of them was discordant DZ with a negative family history (Hublin et al. 1994). In the literature, 16 MZ pairs with at least one affected twin have been reported and five of these pairs were concordant for narcolepsy (Mignot 1998). Although narcolepsy is likely to have a genetic predisposition, the low rate of concordance in narcoleptic MZ twins indicates that environmental factors play an important role in the development of the disease.

To further characterize the DQB1 region in narcoleptic subjects, novel polymorphic markers were isolated and characterized (Mignot et al. 1997a). The markers tested included six novel microsatellite markers (DQCAR, DQCARII, G51152, DQRIV, T16CAR, and G411624R). DQA1, a DQ gene whose product is known to pair with DQB1-encoding polypeptides to form the biologically active DQ heterodimer molecule, was also studied. The results obtained are summarized in Figure1. The association with narcolepsy decreases in theT16CAR–DQB2 region (Mignot et al. 1997a) and in the DRB1 region (Mignot et al. 1994, 1997b). The G411624R andT16CAR microsatellites are complex repeats with drastically different sizes, all of which are frequently observed in narcolepsy susceptibility haplotypes, a result suggesting crossovers in the region. In the DRB1 region, association with narcolepsy is still tight with DRB1*1501 (DR2) in Caucasians and Asians but is significantly lower in African–Americans, which suggests crossovers in the region among ethnic groups.

Figure 1.

Schematic summary of the narcolepsy susceptibility region within the HLA complex. Genes and markers are depicted by vertical bars, alleles observed in narcoleptic patients are listed above each marker.DQB2, DQB3, DQB1, DQA1, andDRB1 are HLA genes and pseudogenes. QBP and QAP are the promoter regions ofDQB1 and DQA1, respectively. G411624R, T16CAR, G51152, DQCAR, and DQCARII are microsatellite CA repeats identified in the HLA DQ region (Mignot et al. 1997a).DQRIV is a compound tandem repeat of 4- and 2-bp units located between DQB1 and G51152. TheDQA1*0102allele is subdivided into 01021 and 01022 based on a codon 109 synonymous substitution. Genomic segments in which frequent recombination was detected are indicated by vertical solid lines. Broken lines indicate rare possible ancestral crossovers detected in the area. Crossovers betweenT16CAR and G51152 occur within ethnic groups; crossovers between QAP and DRB1are frequently observed among ethnic groups (Mignot et al. 1997a). Note that the genomic region shared by most narcoleptic patients extends from a region between T16CAR and G51152 to a region between QAP andDRB1. No other genes were found in 86 kb of genomic sequence surrounding the DQB1*0602 gene (Ellis et al. 1997). Additional diversity is also found at the level ofG51152 andDQRIV, this being most likely due to a slippage mechanism rather than crossover (Lin et al. 1997; Mignot et al. 1997a). (+, Δ, *) Frequent alleles found predominantly in Caucasian, Asian, and African–American populations, respectively; (kb) kilobase pairs. Alleles frequently observed in theDQB1*0602/DQA1*0121 haplotype are underlined.DRB1*1501, DRB1*1503, and DRB1*1602 are DR2subtypes.DRB1*1101 and DRB1*12022 are DR5 subtypes.

The DQA1*0102/DQB1*0602 haplotype is common in narcoleptic patients (Mignot et al. 1994). Other haplotypes withDQA1*0102but not DQB1*0602, such as DQA1*0102andDQB1*0604, are frequent in control populations in all ethnic groups and do not predispose to narcolepsy. DQA1*0102 alone is thus not likely to confer susceptibility but may be involved in addition to DQB1*0602 for the development of narcolepsy (Mignot et al. 1994, 1997a).

Microsatellite analysis in the HLA DQ region revealed that only the area surrounding the coding regions of DQB1 andDQA1 is well conserved across all susceptibility haplotypes. Polymorphism can be observed in microsatellite and/or in the promoter regions flanking the DQB1*0602 and DQA1*0102alleles and in the region between these two genes (Mignot et al. 1997a). Mutations by slippage for some loci, and rare ancestral crossovers in a few instances, contribute to this diversity (Mignot et al. 1997a). Sequence analysis of DQ genes from narcoleptic and control individuals has revealed no sequence variation that correlates with the disease (Lock et al. 1988; Uryu et al. 1989; Ellis et al. 1997;Mignot et al. 1997a). No new gene was found in 86 kb of genomic sequence surrounding the HLA DQ gene (Ellis et al. 1997). A study on the dosage effect of DQB1*0602 allele on narcolepsy susceptibility revealed that DQB1*0602 homozygous subjects are at two to four times greater risk than heterozygous subjects for developing narcolepsy (Pelin et al. 1998). Taken together, these results strongly suggest that the DQA1*0102 andDQB1*0602alleles themselves rather than an unknown gene in the region are the actual susceptibility genes for narcolepsy.

HLA DQB1*0602 Is Neither Sufficient nor Necessary for the Development of Narcolepsy

Of the general population, 12%–38% carry HLADQB1*0602, yet narcolepsy affects only 0.02%–0.18% of the general population. No sequence variation that correlates with the disease was detected in sequence analysis of DQ genes. Nevertheless, a few narcoleptic patients with cataplexy do not carry the DQB1*0602 allele (Mignot et al. 1992, 1997a). HLADQB1*0602 is thus neither necessary nor sufficient for development of narcolepsy–cataplexy.

…….

Canine Narcolepsy as a Model for the Human Disorder

….narcolepsy was identified in numerous canine breeds, including Doberman pinschers, Labrador retrievers, miniature poodles, dachshunds, beagles, and Saint Bernards. All animals display similar symptoms, but the age of onset, severity, and the clinical course vary significantly among breeds (Baker et al. 1982).

….Similar to human narcoleptic patients, animals affected with the disorder display emotionally triggered cataplexy, fragmented sleep, and increased daytime sleepiness. Sleep paralysis and hypnagogic hallucinations cannot be documented because of difficulties in assessing the symptoms in canines. The validity of this model of narcolepsy has also been established through neurophysiological and neuropharmacological similarities with the human disorder. Pharmacological and neurochemical studies suggest abnormal monoaminergic and cholinergic mechanisms in narcolepsy both in human and canines (Aldrich 1991; Nishino and Mignot 1997, 1998). Interestingly, it is also possible to induce brief episodes of cataplexy in otherwise asymptomatic canarc-1 heterozygous animals using specific drug combinations (Mignot et al. 1993).

……

Narcolepsy is both a significant medical problem and a unique disease model. Research in humans has led to the identification of specific HLA alleles that predispose to the disorder. This has suggested the possibility that narcolepsy may be an autoimmune disorder, a hypothesis that has not been confirmed to date. Cells of the central and peripheral nervous systems and immune systems are known to interact at multiple levels (Morganti-Kossmann et al. 1992; Wilder 1995). For example, peripheral immunity is modulated by the brain via autonomic or neuroendocrinal interactions, whereas the immune system affects the nervous system through the release of cytokines. Cytokines have been shown to modulate sleep directly and have established effects on neurotransmission and neuronal differentiation (Krueger and Karnovsky 1995; Mehler and Kessler 1997). It is therefore possible that neuroimmune interactions that are not autoimmune in nature might be involved in the pathophysiology of narcolepsy.

Much less progress has been made in the noncircadian aspect of sleep regulation. Sleep can only be recognized and characterized electrophysiologically in mammals and birds, and single gene mutants for this behavior have not been described in the mouse. Canine narcolepsy is the only known single gene mutation affecting sleep state organization as opposed to circadian control of behavior. The molecular cloning of this gene may lead to a better understanding of the molecular basis and biological role of sleep, as has been the case for circadian rhythms following the cloning of frq, per, and Clock.

Ci-HangYang

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China

Contribution: Acquisition of data

No competing interests declared

</div>”>Ci-HangYang,

PingDong

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China

Contribution: Acquisition of data, Analysis and interpretation of data

No competing interests declared

</div>”>PingDong,

YueLi

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China

Contribution: Acquisition of data, Drafting or revising the article

No competing interests declared

</div>”>YueLi,

YingZhang

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China

Contribution: Drafting or revising the article

No competing interests declared

</div>”>YingZhang,

PingJiang

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China

Contribution: Acquisition of data

No competing interests declared

</div>”>PingJiang,

Darwin KBerg

Neurobiology Section, Division of Biological Sciences, Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, United States

Contribution: Drafting or revising the article

No competing interests declared

</div>”>Darwin KBerg,

ShuminDuan

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China

Contribution: Drafting or revising the article

No competing interests declared

</div>”>ShuminDuan,

Xiao-MingLi

Department of Neurobiology, Institute of Neuroscience, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Collaborative Innovation Center for Brain Science, Zhejiang University School of Medicine, Hangzhou, China; Soft Matter Research Center, Zhejiang University, Hangzhou, China

Contribution: Conception and design, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

Cholinergic projections from the basal forebrain and brainstem are thought to play important roles in rapid eye movement (REM) sleep and arousal. Using transgenic mice in which channelrhdopsin-2 is selectively expressed in cholinergic neurons, we show that optical stimulation of cholinergic inputs to the thalamic reticular nucleus (TRN) activates local GABAergic neurons to promote sleep and protect non-rapid eye movement (NREM) sleep. It does not affect REM sleep. Instead, direct activation of cholinergic input to the TRN shortens the time to sleep onset and generates spindle oscillations that correlate with NREM sleep. It does so by evoking excitatory postsynaptic currents via α7-containing nicotinic acetylcholine receptors and inducing bursts of action potentials in local GABAergic neurons. These findings stand in sharp contrast to previous reports of cholinergic activity driving arousal. Our results provide new insight into the mechanisms controlling sleep.

Epigenetic changes are stable and long-lasting chromatin modifications that regulate genomewide and local gene activity. The addition of two methyl groups to the 9th lysine of histone 3 (H3K9me2) by histone methyltransferases (HMT) leads to a restrictive chromatin state, and thus reduced levels of gene transcription. Given the numerous reports of transcriptional down-regulation of candidate genes in schizophrenia, we tested the hypothesis that this illness can be characterized by a restrictive epigenome.

METHODS We obtained parietal cortical samples from the Stanley Foundation Neuropathology Consortium and lymphocyte samples from the University of Illinois at Chicago (UIC). In both tissues we measured mRNA expression of HMTs GLP, SETDB1 and G9a via real-time RT-PCR and H3K9me2 levels via western blot. Clinical rating scales were obtained from the UIC cohort.

RESULTS A diagnosis of schizophrenia is a significant predictor for increased GLP, SETDB1 mRNA expression and H3K9me2 levels in both postmortem brain and lymphocyte samples. G9a mRNA is significantly increased in the UIC lymphocyte samples as well. Increased HMT mRNA expression is associated with worsening of specific symptoms, longer durations of illness and a family history of schizophrenia.

CONCLUSIONS These data support the hypothesis of a restrictive epigenome in schizophrenia, and may associate with symptoms that are notoriously treatment resistant. The histone methyltransferases measured here are potential future therapeutic targets for small molecule pharmacology, and better patient prognosis.

Schizophrenia is conceptualized as a disorder of gene transcription and regulation. Consequently, chromatin is the ideal scaffold to examine this manifested pathophysiology of schizophrenia, as it constitutes the interface between the underlying genetic code and its surrounding biochemical environment. Through post-transcriptional modifications of histone proteins, gene expression can be either transcriptionally active in a ‘euchromatic’ environment, temporarily quieted in ‘facultative heterochromatin,’ or completely silenced in ‘constitutive heterochromatin’ (Zhang and Reinberg 2001). Post-translational modifications to lysine 9 of the H3 protein (H3K9) are uniquely able to reflect these three levels of transcriptional regulation. H3K9 modifications located in the promoter regions of actively transcribed genes are often acetylated (H3K9acetyl). Conversely, quieted transcription in gene-rich areas of the genome are often associated with mono- or dimethyl H3K9 (H3K9me2), while completely silenced areas of the genome are associated with trimethylated H3K9 (H3K9me3). In particular, the formation of H3K9me2 is catalyzed by histone methyltransferases (HMTs), including Eu-HMTase2 (G9a), Eu-HMTase1 (GLP), and SETDB1 (Krishnan et al. 2011) The different degrees of lysine methylation are possible due to the cooperation of these HMTs, which are able to form large heteromeric complexes (Fritsch et al. 2010).

H3K9 methylation has not been extensively studied in the brain, and until recently the regulation and role of the enzymes responsible for its formation were not known. Postnatal, neuronal-specific GLP/G9a knockdown produces a significant decrease in global H3K9me2 levels and inappropriate gene expression, leading to deficits in learning, reduction in exploratory behaviors and motivation in mice (Schaefer et al. 2009; Shinkai and Tachibana 2011;Tachibana et al. 2005;Tzeng et al. 2007). In humans, deletions or loss-of-function mutations of G9a results in Kleefstra Syndrome, characterized by a severe learning disability and developmental delay (Nillesen et al. 2011; Kleefstra et al. 2005). In humans, increased SETDB1 mRNA expression and resultant elevated H3K9me3 levels have been documented in Huntington Disease (HD) (Ryu et al. 2006; Fox et al. 2004).

A hallmark of schizophrenia is aberrant gene regulation, with the vast majority of studies reporting a down-regulation of gene transcription, suggesting that the epigenome of patients with schizophrenia is restrictive (Akbarian et al. 1995;Guidotti et al. 2000;Fatemi et al. 2005; Impagnatiello et al. 1998; Jindal et al. 2010). Postmortem brain studies indicate a reduction of an open histone modification, H3K4me3, and elevated expression of the histone deacetylase HDAC1 mRNA expression (Cheung et al. 2010; Sharma et al. 2008). The use of peripheral blood mononuclear cells as a reflection of overall chromatin state or at particular gene promoters has been successfully implemented in clinical studies of subjects afflicted depression, alcoholism, and schizophrenia. Peripheral blood cell studies have indicated that schizophrenia is associated with an abnormally condensed chromatin structure; (Issidorides et al. 1975; Kosower et al. 1995) specifically increased restrictive H3K9me2 and reduced H3K9 acetylation (Gavin et al. 2009b). Additionally, H3K9 acetylation in schizophrenia patients is less responsive to in vivo treatment with HDAC inhibitors when compared to both patients with bipolar disorder and nonpsychiatric controls (Sharma et al. 2006;Gavin et al. 2008). Finally, a correlation exists between age of onset of psychiatric symptoms of schizophrenia and baseline levels of H3K9me2 (Gavin et al. 2009b). It is the hypothesis of this paper that schizophrenia can be characterized by a restrictive epigenome, which is observable in both brain and peripheral blood, and has specific and observable effects on psychopathology. We have focused on levels of H3K9me2, indicative of facultative heterochromatin, and the enzymes that catalyze this modification, in patients with schizophrenia to examine their role in this illness.

3.1. mRNA Levels of HMT Gene Expression

We performed a multiple linear regression with each HMT gene of interest as the dependent variable. For postmortem brain tissue we examined sex, age, pH, RIN and diagnosis, whereas for lymphocytes we examined sex, age, and diagnosis as explanatory variables. In these two cohorts, we found that a diagnosis of schizophrenia is a significant predictor for GLP mRNA expression in both postmortem brain samples (β=0.44, F(1,24)=5.80, p<0.05), and in lymphocytes (β=−0.41, F(1,40)=7.91, p<0.01), indicating that patients with schizophrenia demonstrated increased levels compared to nonpsychiatric controls (Fig. 1a). Similarly, a diagnosis of schizophrenia is also a significant predictor for increased SETDB1 mRNA levels in both postmortem brain samples (β=0.39, F(1, 24)=4.33,p<0.05), and in lymphocytes (β=0.37, F(1,40)=6.19, p<0.05; Fig. 1b). A diagnosis of schizophrenia is not a significant predictor for elevated G9a mRNA levels in postmortem brain samples (β=0.22, F(1,24)=1.22, p=ns), but is for lymphocytes (β=−0.317, F(1,40)=4.46, p<0.05; Fig. 1c).

mRNA expression in both postmortem parietal cortical samples from the Stanley Foundation Neuropathology Consortium (on the left) and lymphocyte samples from University of Illinois at Chicago (on the right) and a. GLP mRNA levels, b. G9a mRNA levels and …

To establish whether there exist differences in HMT mRNA among schizophrenic patients taking psychotropic medication, and those who were not, we performed a second multiple linear regression analysis on each individual cohort. The overall or type-specific use of antipsychotic, antidepressant or mood stabilizing medication are not significant predictors of HMT mRNA levels in either the postmortem or the lymphocyte cohorts.

3.2. H3K9me2 levels in the Postmortem Brain

In a previously published study we documented elevated global H3K9me2 levels in lymphocytes obtained from schizophrenia patients compared to nonpsychiatric controls (Gavin et al. 2009b). In the current study we attempted to discern whether this abnormality in a restrictive histone modification is present in brain tissue from the SFNC cohort as well. We performed a multiple linear regression with H3K9me2 levels as the dependent variable, with sex, age, and diagnosis as explanatory variables. We found that diagnosis of schizophrenia is a significant predictor of H3K9me2 levels extracted from postmortem brain tissue (β=0.40, F(1,24)=4.58, p<0.05; Fig. 2). GLP (r=0.65, p<0.001) and SETDB1 (r=0.44,p<0.05) are positively correlated with H3K9me2 levels, as discovered through a Pearson Correlation (data not shown).

H3K9me2 levels are significantly increased parietal cortical samples from patients with schizophrenia when compared to nonpsychiatric controls. Below graph, a representative western blot image is shown. All data is shown as a ratio of optical density …

3.3. Clinical Correlates with Lymphocyte HMT mRNA Levels

Lymphocyte levels of G9a mRNA demonstrated a positive correlation with the PANSS negative subscale total (r=0.61, p<0.05; Fig. 3a), GLP mRNA is positively correlated with the PANSS general subscale total, (r=0.64, p<0.01; Fig. 3b), and SETDB1 mRNA is more highly expressed in patients with longer durations of illness compared to both normal controls and patients in the ‘first episode psychosis’ group (ANOVA, F(2,30)=3.66, p<0.01; Fig. 3c). Patients with a family history of schizophrenia also had significantly increased levels of lymphocyte SETDB1 mRNA (t18=2.52, p<0.05; Fig. 3d).

The current paper demonstrates an increase in GLP and SETDB1 mRNA in both postmortem parietal cortex and lymphocyte samples from patients with schizophrenia, as well as an increase in G9a mRNA in lymphocytes. G9a and GLP are responsible for the bulk of H3K9me2 modifications across the genome (Shinkai and Tachibana 2011; Tachibana et al. 2005), and SETDB1 is the only euchromatic HMT to specifically di- and tri-methylate H3K9 (Zee et al. 2010;Wang et al. 2003), but all three of these HMTs are able to form large heteromeric complexes, thus allowing for the sequential degrees of lysine methylation (Fritsch et al. 2010). Further, we demonstrate that the ultimate outcome of their catalytic activity, H3K9me2, is significantly increased in patients with schizophrenia as compared to nonpsychiatric controls. Moreover, GLP and SETDB1 mRNA are positively correlated with H3K9me2 levels. These findings add gravity to our previous demonstration of increased H3K9me2 levels in lymphocytes from schizophrenic patients (Gavin et al. 2009b).

Our investigations into the role of H3K9me2 in schizophrenia pathophysiology, as opposed to other H3K9 modifications, were motivated by the hypothesis that initial inactivation of gene promoter activity at various schizophrenia candidate genes can result in gradual entrenchment of the heterochromatin state as a result of disease chronicity and disuse (Sharma et al. 2012). Areas of H3K9me2 can then act as a platform for additional restrictive adaptors, thus resulting in the spreading of heterochromatin across previously unmodified gene rich areas. As such, the gene altering effects of medications are unable to overcome this restrictive burden, leading to repeated medication failures (Sharma et al. 2012). Support for this hypothesis has been previously demonstrated, (Sharma et al. 2008; Benes et al. 2007) including the finding that schizophrenia patients clinically treated for four weeks with the HDAC inhibitor, valproic acid, displayed no increase in peripheral blood cell acetylated histones 3 or 4 as compared to bipolar patients (Sharma et al. 2006). Here, we find an increase in both H3K9me2 levels and the enzymes which catalyze this modification, providing additional evidence supporting an increased heterochromatin state in schizophrenia.

The major role of the parietal cortex is to integrate and evaluate sensory information (Andersen & Buneo, 2003; Cohen & Andersen, 2002). It is one of the last areas of the human brain to fully mature, (Geschwind, 1965) thus early life environmental insults could have a profound effect. Disordered thought, a common symptom in schizophrenia, is most likely explainable through disruption of this system (Torrey, 2007). Patients with schizophrenia report either acute (McGhie & Chapman, 1961) or blunted (Freedman, 1974) sensitivity to sensory stimuli, and demonstrate overall impairment of sensory integration (Manschreck & Ames, 1984; Torrey, 1980). Similar patterns of transcriptional regulation are observed across the cortex, consequently, results from the parietal cortex likely reflect patterns of gene transcription in other brain regions (Hawrylycz et al., 2012).

Due to its heterogeneity, examining schizophrenia as a binary measurement of illness when examining biological relevancy can be limiting (Arango et al. 2000;Buchanan and Carpenter 1994). Through utilizing the PANSS, biological underpinnings that do not demarcate cleanly with diagnostic categories, can be correlated directly with specific symptomatology. Correlations between methyltransferase enzymes and clinical symptomatology indicate that these restrictive enzymes could contribute to specific facets of the illness, particularly negative and general symptoms, which are particularly resistant to improvement. Increased severity of negative symptoms are correlated with poorer disease prognosis, (Wieselgren et al. 1996) and are not alleviated through our current regimen of psychotropics.

Additionally, SETDB1 mRNA levels are also correlated with other markers of a worse disease prognosis, including a more chronic form of the illness, and a history of schizophrenia in the family. Pharmacological targeting of increased levels of SETDB1improves motor performance and extends survival in HD mice, indicating the promise of treatments that modulate gene silencing mechanisms in neuropsychiatric disorders (Ryu et al. 2006).

The main weakness of this current study was that clinical symptoms were correlated with mRNA extracted from peripheral tissue. Enzymes relating specifically to synaptic function were not examined, but rather overall mechanisms of epigenetic regulation that are not tissue specific. While postmortem investigations are able to serve as a useful snapshot at the time of death, the ability to measure and monitor histone marks over time as marker of disease progression, improvement, or as a predictor of pharmacological response are only possible using peripheral blood cells. A strong rationale for the use of blood chromatin ‘levels’ as a type of biosensor that registers the epigenetic milieu has been proposed elsewhere (Sharma 2012). Furthermore, previous studies have indicated the mRNA patterns of expression patterns in lymphocytes are capable of distinguishing between psychiatric diagnostic groups (Middleton et al. 2005).

The present study hypothesized that schizophrenia may be due to abnormal regulation of fundamental epigenetic mechanisms, thus, we chose to measure overall levels of H3K9me2 opposed to specific gene promoters, based on the assumption that while the individual genes silenced in the brain and blood may not be the same, similar global pathogenic processes may be occurring in both tissues.

The results of this paper indicate that chromatin is more restrictive in patients with schizophrenia, and may be significantly contributing to disease pathology. If, through pharmacological interventions, a reduction in this histone hyper-restrictive insult in schizophrenia can be relaxed, inducing a type of “genome softening,” then neuronal gene expression can be enhanced, thus allowing for increased plasticity and improved therapeutic response (Sharma 2005).

Alterations in histone lysine methylation and other epigenetic regulators of gene expression contribute to changes in brain transcriptomes in mood and psychosis spectrum disorders, including depression and schizophrenia. Genetic association studies and animal models implicate multiple lysine methyltransferases (KMTs) and demethylases (KDMs) in the neurobiology of emotion and cognition. Here, we review the role of histone lysine methylation and transcriptional regulation in normal and diseased neurodevelopment and discuss various KMTs and KDMs as potential therapeutic targets in the treatment of neuropsychiatric disease.

Schizophrenia and depression are major psychiatric disorders that lack consensus neuropathology and, in a large majority of cases, a straightforward genetic risk architecture. Furthermore, many patients on the mood and psychosis spectrum show an incomplete response to conventional pharmacological treatments which are mainly aimed at monoamine signaling pathways in the brain (Box 1).

Box 1 Schizophrenia and Depression

Schizophrenia affects 1% of the general population and typically begins during young-adult years, although cognitive disturbances could be evident much earlier. The disease is, in terms of genetics and etiology, highly heterogeneous, and increasingly defined as different and partially independent symptom complexes: (i) psychosis with delusions, hallucinations and disorganized thought; (ii) cognitive dysfunction including deficits in attention, memory and executive function; and (iii) depressed mood and negative symptoms including inability to experience pleasure (anhedonia), social withdrawal and poor thought and speech output [42]. Currently prescribed antipsychotics, which are mainly aimed at dopaminergic and/or serotonergic receptor systems, exert therapeutic effects on psychosis in approximately 75% of patients. However, it is the cognitive impairment which is often the more disabling and persistent feature of schizophrenia [42]. Currently there are no established pharmacological treatments for this symptom complex. However, given that cognitive dysfunction is an important predictor for long-term outcome, this area is considered a high priority in schizophrenia research, as reflected by initiatives combining efforts from government agencies, academia and industry, including MATRICS (the Measurement and Treatment Research to Improve Cognition in Schizophrenia) [42].

Affective disorders as a group show, in terms of genetic risk architecture, some overlap with schizophrenia. For example, rare structural variants, including the balanced translocation at the Disrupted-in-Schizophrenia 1 (DISC-1) locus (1q42) or the 22q11 deletion are, in different individuals, associated with either mood disorder or schizophrenia [81, 82].

Depression, including its more severe manifestation, major depressive disorder which has a lifetime risk of 10–15% for the U.S. general population, is associated with excessive sadness, anhedonia, negative thoughts, and neurovegetative symptoms including changes in sleep pattern and appetite [1]. The disorder, which in more severe cases is accompanied by delusions, hallucinations and other symptoms of psychosis, often takes a chronic and recurrent course. Conventional antidepressant therapies primarily target monoamine metabolism and reuptake mechanisms at the terminals of serotonergic, noradrenergic and dopaminergic neurons. Unfortunately, up to 40% of cases show an insufficient response to these pharmacological treatments [1]. In addition, many antipsychotic and antidepressant drugs have significant side effect burden, including weight gain, diabetes and metabolic defects, extrapyramidal symptoms and sexual dysfunction [83, 84].

However, there is evidence that dysregulated gene transcription, indicative of compromised neural circuitry, contributes to disordered brain function in psychosis and mood spectrum disorder [1, 2]. While no single gene transcript is consistently affected, alterations in RNA levels contribute to defects in GABAergic inhibitory neurotransmission and more generally, synapse organization and function, metabolism and mitochondrial functions, and oligodendrocyte pathology [3–5]. While a number of transcriptional and post-transcriptional mechanisms may contribute to these changes, chromatin-associated proteins and epigenetic regulators invoked in sustained alterations of gene expression and function (Box 2) could play a critical role in the pathophysiology, or treatment of mental illness [6,7]. Indeed, there is evidence that changes in acetylation of histone lysine residues, which are broadly associated with active gene expression [8] and considered a potential therapeutic target for cancer and other medical conditions [9], also impact gene expression patterns in the brain and thereby influence emotional and cognitive functions. For example, mice or rats exposed to systemic treatment, or localized intracranial injections of class I/II histone deacetylase inhibitors (HDACi) exhibit behavioral changes reminiscent of those elicited by conventional antidepressant drugs [10–13]. The short chain fatty acid derivative valproic acid, widely prescribed for its mood-stabilizing and anticonvulsant effects, induces brain histone hyperacetylation at a select set of gene promoters when administered to animals at comparatively high doses [14]. Conversely, overexpression of selected HDACs in neuronal structures implicated in the neurobiology of depression, including the hippocampus, elicit a pro-depressant behavioral phenotype [12]. Similarly, animals treated with class I/II HDACi often show improved performance in learning and memory paradigms and furthermore, drug-induced inhibition or activation of class III HDAC (also known as sirtuins) elicits changes in motivational and reward-related behaviors [15]. Therefore, the orderly balance between histone acetyl-transferase and deacetylase activities is critical for cognitive performance and synaptic and behavioral plasticity [16]. Likewise, However, HDACs interfere with acetylation of many non-histone proteins in the nucleus and cytoplasm [16], and moreover, some of these drugs carry a significant side effect burden [9]. Therefore, in light of the emerging role of epigenetic mechanisms in the neurobiology of these and other psychiatric conditions [6], the therapeutic potential of chromatin modifying drugs, other than the HDACi, warrants further investigations. This review will focus on histone lysine methylation, one of the most highly regulated chromatin markings in brain and other tissues. Multiple methyltransferases (KMTs) and demethylases (KDMs) were recently implicated in emotional and cognitive disorders (Fig. 1), and these types of chromatin modifying enzymes could emerge as novel targets in the treatment of mood and psychosis spectrum disorders.

Box 2 Epigenetic regulators and chromatin structure and function

Epigenetics, in the broader sense, applies both to dividing and postmitotic cells, and refers to a type of cellular memory that involves sustained changes in chromatin structure and function, including gene expression, in the absence of DNA sequence alterations (For in depth discussion, see [85]). Chromatin is essentially a repeating chain of nucleosomes comprised of genomic DNA wrapped around an octamer of core histones H2A/H2B/H3/H4. The histone proteins are intensely decorated with epigenetic information, with more than 70 (amino acid) residue-specific sites subject to various types of post-translational modifications (PTM). These include lysine (K) acetylation, methylation and poly ADP-ribosylation, arginine (R) methylation, and serine (S), threonine (T), tyrosine (Y) and histidine (H) phosphorylation [86]. In addition, a subset of the histone H2A, H2B and H4 lysines are covalently linked to the small protein modifiers ubiquitin and SUMO [87, 88]. Finally, epigenetic markings in genomic DNA include 5-methyl-cytosine and the related form, 5-hydroxy-methyl-cytosine [85]. These DNA and nucleosomal histone markings define the functional architecture of chromatin (see main text).

Proteins associated with methylation and other histone PTM are typically defined either as ‘writers’, ‘erasers’ or ‘readers’, essentially differentiating between the process of establishing or removing a mark as opposed to providing a docking site for chromatin remodeling complexes that regulate transcription, or induce and maintain chromatin condensation [18, 86, 89]. As it pertains to the brain, especially in the context of neuropsychiatric disease, a substantial body of knowledge has been generated for a select set of site-specific (K) methyltransferases and demethylases (Fig. 1A). In contrast, many PTMs are recognized by large numbers of reader proteins [90], but to date only very few of these readers have been explored in the brain. To mention just two examples, there are approximately 75 reader proteins specifically associated with histone H3-trimethyl-lysine 4 (H3K4me3), including several components of the SAGA complex ascribed with a key role for transcriptional initiation at RNA polymerase II target genes [90]. In contrast, H3K9me3, generally considered a repressive mark, provides a central hub for heterochromatin (associated) proteins including several members of the HP1 family and zinc finger domain containing molecules [90]. There is additional complexity because pluripotent stem cells and additional cell types decorate many of their promoters with ‘bivalent domains’ which include both open chromatin-associated (methylated H3K4 and H3/H4 acetylation) and repressive (methylated H3K27) marks [91, 92].

Regulation of histone (K) methylation. (A) Listings of residue-specific KMTs and KDMs for H3K4/9/27/36/79 and H4K20. The majority of KMT and KDM are highly specific for a single histone residue, while a few enzymes target multiple residues, as indicated. Red marked KMT/KDM are implicated in neurodevelopment or psychiatric disease as discussed in main text. The non-catalytic JARID2 regulates activity and function of related KMTs. (B) Simplified scheme for selected mono- and trimethylated histone lysine markings implicated in transcriptional regulation, silencing and enhancer function.

The methylation of lysine and arginine residues, like other histone PTM, define chromatin states and function [8, 17]. To date, more than 20 methyl-marks on K and R residues have been described [18, 19]. As it pertains to the lysines, the majority of studies focused on the regulation and methylation-related functions of six specific sites: H3K4, H3K9, H3K27, H3K36, H3K79 and H4K20 [18]. For H3K4 and H3K9/K27, there is additional complexity because specific information is also conveyed (i) for H3K4, the unmethylated lysine effectively serving as a DNA methylation signal [20, 21], and (ii) for H3K9/K27 acetylation as an alternative PTM [22, 23] (Fig. 1A). For the aforementioned H3/H4 residues, specific biological functions and their interrelations with functional chromatin states, including transcriptional initiation and elongation, heterochromatic silencing and other mechanisms, have been described for the trimethyl-, and for some of the mono- and dimethyl-modifications (Representative examples are provided in Fig. 1B. See ref [17] for a detailed description of the histone methylation code and its relation to other types of histone PTM).

The following examples further illustrate the complex regulation of histone lysine methylation. Monomethylation of histone H3-lysine 4 (H3K4me1) plays an important role in neuronal activity-induced transcription at enhancer sequences [24], but the related forms, H3K4me3/2 are primarily found at the 5′ end of genes, with H3K4me3 mostly arranged as distinct and sharp peaks within 1–2Kb of transcription start sites. The H3K4me3 mark provides a docking site at the 5′ end of genes for chromatin remodeling complexes that either facilitate or repress transcription [25]. Furthermore, mono-methyl-H4-K20 shows strong positive correlation with gene expression at promoters enriched with CpGs, which contrasts to the trimethylated form of the same residue which generally is associated with repressed chromatin [23]. Taken together, these examples illustrate that even closely related histone lysine methylation markings are potentially associated with very different chromatin states.

To date, H3K4, H4K9, H3K27 and H4K20 methylation signals were measured at specific loci and genome-wide in human brain, essentially confirming that each of these epigenetic markings defines the same type of chromatin as in the peripheral tissues or animal brain [26–30]. Interestingly, a subset of psychotherapeutic drugs including the mood-stabilizer valproate, the atypical antipsychotic clozapine and some monoamine oxidase inhibitors and stimulant drugs interfere with brain histone methylation (Table 1).

Molecular mechanisms of histone (lysine) methylation

A complex system of site-specific methyltransferases, which transfer the methyl-group of S-Adenosyl-Methionine (SAM) to lysine residues, has evolved in the vertebrate cell. There are an estimated 70 human genes harboring the Su(var)3–9,Enhancer of Zeste,Trithorax (SET) domain, which spans approximately 130 amino acids essential for KMT enzymatic activity [31]. The only known exception is the H3K79-specific methyltransferase, KMT4/DOT1L [31, 32], which lacks a SET. Each of the histone K residues discussed above is the preferential target of a distinct set of methyltransferase proteins (Fig. 1A)[19]. Of note, these histone-modifying enzymes are thought not to access histone substrates directly unless recruited by DNA-bound activators and repressors, a mechanism which could target each methyltransferase to a highly specific set of genomic loci [19].

An equally complex system exists for the site-specific lysine demethylases (Fig. 1A). There are at least two different mechanisms for active histone demethylation. The first enzyme type, represented by lysine-specific demethylase 1 (LSD1/KDM1A), contains an amine oxidase domain and requires flavin adenine dinucleotide (FAD) as a cofactor to demethylate di- and mono-methylated lysines. LSD1 and its homologue, LSD2, are primarily H3K4 demethylases, albeit depending on species and context, and activity against H3K9 also has been described [18]. Interestingly, monoamine oxidase inhibitors (MAOi) such as tranylcypromine or phenelzine — powerful antidepressants that exert their therapeutic effects mainly by elevating brain monoamine levels through inhibition of MAO-A/B — also block LSD1 type histone demethylases [18]. While LSD1 is thought to regulate histone methylation at promoters, LSD2 is bound to transcriptional elongation complexes and removes H3K4 methyl markings in gene bodies, thereby facilitating gene expression by reducing spurious transcriptional initiation outside of promoters [33]. The second type of demethylase, which in contrast to LSD1/LSD2 is capable of demethylating trimethyl markings, involves Fe2+-dependent dioxygenation by Jumonji-C (JmJC) domain-mediated catalysis [18]. Given that each of the KMTs and KDMs described has a different combinatorial set of functional domains and (protein) binding partners [18, 34], it is likely that the various site-specific methyltransferases and demethylases are largely non-redundant in function.

KMTs and KDMs with a role in cognition and neuropsychiatric disease

An increasing number of KMTs and KDMs are implicated in neurodevelopment and major psychiatric diseases (marked in red in Fig. 1A).

H3K4

The first histone lysine methyltransferase explored in the nervous system was KMT2A/MLL1, a member of the mixed-lineage leukemia (MLL) family of molecules. Mice heterozygous for an insertional (lacZ) loss-of-function Mll1mutation show distinct abnormalities in hippocampal plasticity and signaling [35], in conjunction with defects of learning and memory [36]. Of note, the hippocampus, and other portions of the forebrain including prefrontal cortex and ventral striatum, are frequently implicated in the neural circuitry of mood and psychosis spectrum disorders [1]. Furthermore, conditional deletion of Mll1resulted in defective neurogenesis during the early postnatal period [37]. While the full spectrum of MLL1 target genes in neurons and glia awaits further investigation, dysregulated expression of certain transcription factors such as DLX2, a key regulator for the differentiation of forebrain GABAergic neurons (which are essential for inhibitory neurotransmission and orderly synchronization of neural networks) [38], may contribute to the cognitive phenotype of the Mll1mutant mice. These observations may be relevant for the pathophysiology of schizophrenia, because some patients show in the prefrontal cortex a deficit in H3K4-trimethylation and gene expression at a subset of GABAergic promoters, including GAD1 encoding a GABA synthesis enzyme [28]. While the timing and age-of-onset for this ‘molecular lesion’ remains unknown, it is of interest that in the normal PFC, H3K4 methylation at the site of GABAergic genes progressively increases during the transition from fetal period to childhood to adulthood [28]. The epigenetic vulnerability of the Gad1 promoter during such prolonged developmental periods is further emphasized by recent animal studies demonstrating that Gad1-DNA methylation and histone acetylation are heavily influenced by the level of maternal care in the neonatal period/pre-weanling period [39].

There is additional evidence that epigenetic fine-tuning of the brain’s H3K4 methyl-markings is critical for orderly neurodevelopment. Of note, loss-of-function mutations in KDM5C/JARID1C/SMCX, an X-linked gene encoding a H3K4 demethylase, have been linked to mental retardation [40] and autism spectrum disorders [41]. The KDM5C gene product operates in a chromatin remodeling complex together with HDAC1/2 histone deacetylases and the transcriptional repressor REST, thereby poising neuron-restrictive silencer elements for H3K4 demethylation and decreased expression of target genes including synaptic proteins and sodium channels [40]. However, because this study was conducted with the HeLa cell line, it remains to be determined whether similar mechanisms operate in the nervous system.

In addition to its role in neurodevelopment, MLL-mediated H3K4 methylation could play a potential role for the treatment of psychosis. The atypical antipsychotic clozapine, which has a somewhat higher therapeutic efficacy when compared to conventional antipsychotics that function primarily as dopamine D2 receptor antagonists [42], upregulates H3K4 tri-methylation at the Gad1/GAD1GABA synthesis enzyme gene promoter. These effects were not mimicked in dopamine receptors D2/D3 (Drd2/3) compound null mutant mice, suggesting that blockade of dopamine D2-like receptors is not sufficient for clozapine-induced H3K4 methylation [28]. In the human PFC, GAD1-associated H3K4 methylation was increased in subjects exposed to clozapine, as compared to subjects treated with conventional antipsychotics. Conversely, mice heterozygous for the H3K4-specific KMT, mixed-lineage leukemia 1 (MLL1), exhibited decreased H3K4 methylation at brain Gad1 [28]. Therefore, it is possible that MLL1, which is highly expressed in GABAergic and other neurons of the adult cerebral cortex [28], will in the future emerge as a novel target for the treatment of psychosis. Questions that remain to be resolved include (i) the molecular pathways linking clozapine — a drug that impacts dopaminergic, serotonergic, muscarinic and other signaling pathways — to MLL1-mediated histone methylation, and (ii) whether or not the clozapine-induced changes in H3K4 methylation are restricted to GABAergic gene promoters or, alternatively, the reflection of more widespread epigenetic changes throughout the genome. Of note, clozapine’s effects on H3K4 methylation require intact brain circuitry and cannot be mimicked in cultured neurons differentiated from forebrain progenitor cells [43]. This finding is in good agreement with the recent observation that some of clozapine’s therapeutic effects require an intact serotonergic system, particularly its presynaptic components [44].

H3K9

The 9q34 subtelomeric deletion syndrome, which includes mental retardation and other developmental defects, is caused by deleterious mutations and haploinsufficiency of euchromatin histone methyltransferase 1 (EHMT1, also known as GLP and KMT1D) [45]. This gene encodes a H3K9-specific methyltransferase that operates in a multimeric complex that includes its closest homologue, G9a/KMT1C, and additional H3K9-specific HMTs [46]. Studies in mutant mice suggest that the GLP/G9a complex is important for suppression of non-neuronal and progenitor genes in mature neurons, and loss of this complex has deleterious effects on cognition and other higher brain functions [47]. Furthermore, G9a-mediated H3K9 methylation events within the reward circuitry, including the ventral striatum, are critical intermediates for the long-term effects of cocaine on reward behavior and neuronal morphology [48]. This would suggest that GLP/G9a, and proper regulation of H3K9 levels, is important for orderly brain function both in developing and mature brain.

Furthermore, changes in motivational and affective behaviors could be elicited by overexpression of the H3K9-HMT, SET domain bifurcated 1 (KMT1C/SETDB1/ESET), in adult forebrain neurons [49]. Interestingly, SETDB1 occupancy in neuronal chromatin is highly restricted, and may be confined to less than 0.75% of annotated genes [49]. However, among these are several NMDA and other ionotropic glutamate receptor subunit genes, including Grin2a/b (Nr2a/b)[49]. Mild to moderate inhibition of NMDA receptor-mediated (including Grin2b) neurotransmission elicits a robust improvement of depressive symptoms in some mood disorder patients [50], and, indeed, SETDB1-mediated H3K9 methylation and repressive chromatin remodeling at the Grin2b locus was associated with antidepressant-like behavioral phenotypes in the Setdb1 transgenic mice [49]. Of note, NMDA receptor antagonists, including GRIN2B-specific drugs, elicit significant therapeutic benefits even in subjects who failed multiple trials of selective serotonin reuptake inhibitors (SSRI) and other conventional antidepressants [50]. However, drugs directly acting at the NMDA receptor site have an unfavorable side effect profile, and therapeutic strategies aimed at SETDB1 expression and activity may therefore provide an alternative strategy.

Interestingly, mice with a genetic ablation of Kap1, encoding the SETDB1 binding partner KRAB-associated protein 1, also known as TRIM28/TIF1b/KRIP1)[51], show increased anxiety and deficits in cognition and memory [52], which are phenotypes that are broadly opposite from those observed in mice with increasedSetdb1 expression in brain [49]. These findings further speak to the therapeutic potential of the Kap1-Setdb1 repressor complex in the context of neuropsychiatric disease.

Finally, the H3K9-specific demethylase, KDM3A/Jmjd1A, showed increased H3K9-methylation at its own promoter in the ventral striatum of mice exposed to social defeat (a type of stressor associated with a depression-like syndrome in these animals), while mice that were treated with a conventional antidepressant or that were resilient to this type of stress did not show changes in KDM3A promoter methylation [53]. While it is unclear whether KDM3A or some other demethylase acitivity is altered in the depressed animals, the same study [53] reported widespread repressive histone methylation changes, including increased dimethyl-H3K9 and methylated H3K27 at hundreds of gene promoters in stress susceptible animals, which further emphasizes the importance of these PTMs for the epigenetics of mood disorder.

H3K27

The H3K27-selective methyltransferase, KMT6A, also known as Enhancer of zeste homolog2 (EZH2), is associated with the polycomb repressive chromatin remodeling complex 2 (PRC2) [54], and essential for cortical progenitor cell and neuron production. Consequently, loss of EZH2 function is associated with severe thinning of the cerebral cortex and a disproportionate loss of neurons residing in upper cortical layers I–IV [55]. Likewise, the H3K27-specific demethylase, JMJD3, is important for neurogenesis and neuronal lineage commitment [56]. Furthermore, H3K27 methylation is dynamically regulated in mature brain and involved in the neurobiology of major psychiatric disease. For example, changes in expression of brain-derived neurotrophic factor (Bdnf) in hippocampus of mice exposed to environmental enrichment or chronic stress are associated with opposite changes in the H3K27me3 mark at a subset of Bdnf gene promoters [12,57]. In addition, acute stress leads to an overall decrease in hippocampal H3K27me3 and H3K9me3 [58]. Furthermore, in the orbitofrontal cortex of suicide completers, alterations in H3K27 methylation were described at the TRKB gene, encoding the high affinity receptor for the nerve growth factor molecule, BDNF [27]. Changes in the balance between histone H3K4 and H3K27 methylation, or DNA cytosine and H3K27 methylation may also contribute to GABAergic gene expression deficits in schizophrenia [28, 43]. To date it remains unclear which of the various H3K27-specific KMTs and KDMs (Fig. 1) are involved in these disease-related alterations in postmortem brain tissue. Of note, the Jumonji and Arid containing protein 2 (JARID2), which by itself lacks catalytic activity but is crucial for subsequent H3K27 or H3K9 methylation by recruiting the polycomb PRC2 complex to its target genes [59, 60], is located within the schizophrenia susceptibility locus on chromosome 6p22 and confers genetic risk in multiple populations of different ethnic origin [61, 62]. While the biological functions of JARID2 have been studied primarily in the context of transcriptional regulation in stem cells [63, 64], this gene shows widespread expression in the mature nervous system [65], implying JARID2-mediated control over polycomb repressive chromatin remodeling in the adult brain.

H3K36 and H4K20

Epigenetic dysregulation of nuclear receptor-binding SET domain containing protein 1/KMT3B could play a role in some neuro- and glioblastomas [66], but like for other H3K36 and H4K20 regulating enzymes (Fig. 1), to date little is known about their role in neurodevelopment, cognition and psychiatric disease. Strikingly, however, KMT3A/HYPB/SETD2, a member of the SET2 family of KMTs mediating H3K36 methylation [67], is also known as huntingtin-interacting protein 1 (HIP-1) or huntingtin(yeast)-interacting protein B (HYPB) [68]. Huntington’s is a triplet repeat disorder and chronic neurodegenerative condition with motor symptoms and cognitive defects, and significant changes in mood and affect [69]. Whether or not there is altered H3K36 methylation in the neuronal populations that are at risk for degeneration is unclear. Furthermore, the huntingtin/KMT3A interaction has been documented for yeast [68] but not brain. Of note, wildtype huntingtin is a facilitator of polycomb complex PCR2-mediated H3K27 methylation [70], and furthermore, H3K4 and H3K9 methylation changes have been reported in preclinical model systems and postmortem brains with Huntington’s disease [71, 72]. Therefore, it is possible that transcriptional dysregulation in this condition is associated with aberrant methylation patterns of multiple lysine residues.

KMTs and KDMs as Novel Drug Targets

Given the emerging role of histone methylation in the neurobiology of psychiatric disease, the next obvious question is whether this type of PTM could provide a target for a new generation of psychotropic therapeutics. In principle, KMTs and KDMs should provide fertile ground for the development of novel drugs, because these enzymes are considered more specific than, for example, HDACs, because each HDAC enzyme is likely to affect a much larger number of histone residues as compared to KMTs/KDMs [73]. However, like for other histone modifying enzymes, the specificity of KMTs and KDMs is not limited to histones but includes the (de)methylation of lysines of non-histone proteins, including the p53 tumor suppressor protein and the VEGF growth factor [74]. Druggable domains within the KMTs and KDMs could involve not only their catalytic sites, such as the SET domain for the KMTs or the amino oxidase and JmjC domains for the LSD1 and JMJD subtypes of KDMs, respectively, but also some of the many other functional domains that are specific to subsets of these proteins [75]. One potential candidate would be the bromodomain of the MLLs and other H3K4-specific methyltransferases [75]. Bromodomains, which are present in many different types of nuclear proteins, bind to acetylated histones and small molecules interfering with some of these interactions recently emerged as powerful modulators of systemic inflammation [76].

The catalytic activity of the SET domain containing KMTs requires the universal methyl donor, S-adenosyl-methionine (also known as AdoMET). Crystallographic and functional studies revealed that the SAM binding pocket of KMTs is different from the SAM pockets of other proteins, which may increase the chance to develop compounds which specifically target histone methyltransferases but not other enzymes and proteins [31]. Currently, however, no KMT or KDM related drug is in clinical trials. However, several of these compounds show therapeutic promise in preclinical studies. For example, the S-adenosylhomocysteine hydrolase inhibitor, 3-deazaneplanocin A (DZNep) induces apoptosis in breast cancer cells [77]. This drug alters H3K27 and H4K20 trimethylation via interference with polycomb PRC2 repressive chromatin remodeling [73]. Antioncogenic effects were also observed with BIX-01294, a drug that downregulates H3K9 methylation levels by binding to the SET domain of the G9a/GLP(EHMT1) methyltransferases [73]. The same drug was shown to alter addictive behaviors and H3K9 methylation when infused locally into the brain of cocaine-exposed mice [48]. As discussed above, while tranylcypromine and other monoamine oxidase inhibitors used for the treatment of depression are weak inhibitors of the LSD1 type of KDM, recently several compounds emerged with much stronger activity against LSD1/LSD2 [18]. It will be extremely interesting to explore these drugs in preclinical models for mood and psychosis spectrum disorders. Finally, microRNA-based therapeutic strategies, aimed at decreasing levels and expression of chromatin remodeling complexes, including some of the histone modifying enzymes discussed here, are gaining increasing prominence in the field of cancer therapy [73] and may in the future emerge as a novel therapeutic option in the context of neuropsychiatric disease.

DNA methylation in mammals is a key epigenetic modification essential to normal genome regulation and development. DNA methylation patterns are established during early embryonic development, and subsequently maintained during cell divisions. Yet, discrete site-specific de novo DNA methylation or DNA demethylation events play a fundamental role in a number of physiological and pathological contexts, leading to critical changes in the transcriptional status of genes such as differentiation, tumor suppressor or imprinted genes. How the DNA methylation machinery targets specific regions of the genome during early embryogenesis and in adult tissues remains poorly understood. Here, we report advances being made in the field with a particular emphasis on the implication of transcription factors in establishing and in editing DNA methylation profiles. J. Cell. Physiol. 230: 743–751, 2015.

DNA methylation is a well-studied epigenetic modification in mammalian genomes, discovered in 1948. It is involved in a number of essential cellular processes such as transcription regulation, cellular differentiation, cellular identity maintenance, X inactivation, gene imprinting, and the cellular response to environmental changes (Klose and Bird, 2006; Guibert and Weber, 2013; Smith and Meissner, 2013; Subramaniam et al., 2014). DNA methylation has proved to be a dynamic process, requiring continuous regulation and potentially having an important regulatory role for tissuespecific differentiation or cellular signaling. Indeed, the analysis of the distribution of DNA methylation at the genome scale, and nowadays at the single-base resolution, in different physiological and pathological states, unraveled that local changes in DNA methylation contribute to cell-type specific variation in gene expression. Furthermore, aberrant DNA methylation patterns are documented in a number of human diseases from Immunodeficiency, Centromere instability, and Facial anomalies (ICF) syndrome to cancer, and contribute to the onset or development of these diseases (Smith and Meissner, 2013; Weng et al., 2013; Subramaniam et al., 2014). Needless to say, these discoveries also fuel the promising idea that therapeutic strategies targeting DNA methylation can be used in the prevention and the treatment of cancer and other human diseases, including neuro-developmental disorders (Weng et al., 2013; Subramaniam et al., 2014). As an example, antipsychotic drugs clozapine and sulpiride, combined with histone deacetylase inhibitor valproate, have a beneficial action in schizophrenia and bipolar patients, maybe because they revert the aberrant DNA methylation status at GABAergic gene promoters (Dong et al., 2008). In 2004, 5-azacytidine (VidazaTM, Celgene Corporation, Summit, NJ). A drug blocking DNA methylation, received approval by the Food and Drug Administration for the treatment of myelodysplastic syndromes (Kaminskas et al., 2005).

Figure 1. Overview of the DNA methylation and demethylation pathway. (A) DNMT1 is responsible for the maintenance of DNA methylation during DNA replication. It recognizes hemi-methylated CpG, thanks to its interaction with co-factor UHRF1, and it adds methylation on the un-methylated strand. Black bubbles: methylated CpG. Empty bubbles: un-methylated CpG. (B) DNMT3A/B are responsible for de novo DNA methylation. They establish new patterns of methylation directly from unmethylated CpG-containing sequences. In the embryo, their activity is modulated by a catalytically inactive family member DNMT3L. (C) Passive demethylation occurs through loss of DNMT1/3 activity in actively dividing cells. Loss can be attributed to post-translational modifications, gene mutations, gene silencing or any other mechanism that will eventually lead to DNMT activity inhibition. (D) Active DNA demethylation is catalyzed by the TET family of enzymes. TET1, 2 and 3 can oxydate 5mC into 5hmC (represented in grey bubbles), and eventually oxidate 5hmC into 5-formylcytosine and 5-carboxy-cytosine. None of these bases is recognized by DNMTs causing loss of DNA methylation during DNA replication. In addition, these oxidated bases are recognized by the base-excision repair (BER) pathway and catalytically removed.

Figure 2. Summary of the nuclear factors and epigenetic marks involved in the maintenance of DNA methylation status in different regions of the genome. The table recapitulates our current knowledge on transcription factors, chromatin remodellers and histone marks contributing to the establishment of DNA methylation and its erasure. The information is presented according to genomic features, sharing common regulators, such as promoters/enhancers, tumor suppressor genes, germline gene promoters, imprinted regions, DNA repeats, and retroviral elements and peri-centromeric regions.

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t KAP1/DNMTs control the maintenance of DNA methylation, independently of DNA replication, on a number of genomic targets. Yet, DNMTs have been shown to be recruited onto the chromatin by other chromatin remodelers, such as SETDB1 or G9a, or secondary to gene silencing (Gibbons et al., 2000; Dennis et al., 2001; Guibert and Weber, 2013; Pacaud et al., 2014). Thus, only the identification of the full-spectrum of transcription factors involved in the regulation of DNA methylation will tell whether this function is predominantly confer to KRAB-ZNF factors. This systematic analysis might help understand why only a limited number of factors per family are involved in the shaping of DNA methylation. In the case of ZNF factors several explanations have been postulated. The resolution of the structure of the ZNF fingers of Zfp57 bound onto methylated DNA indicated that a specific amino-acid sequence in the DNA binding ZNF fingers might be required for the recognition and binding of methylated CpG sequences (Liu et al., 2012; BuckKoehntop and Defossez, 2013). Using this knowledge, researchers have postulated that ZNF factors containing this motif might likely contribute to shape DNA methylation profile (Liu et al., 2013). An alternative hypothesis rely on the observation that KRAB-ZNF factors are present uniquely in vertebrate genomes and have expanded quite dramatically in mammalian genomes. As DNA repeats sequences also quickly evolved in mammalian genomes, it is suggested that humanspecific KRAB-ZNF factors might primarily contribute in DNA repeats silencing (Lukic et al., 2014).

……

DNA methylation plays an important role in the control of gene expression and cell fate in mammals. Its regulation and function has been upon intense scrutiny since its discovery in mid-1900s. Yet, how DNA methylation patterns are established during embryogenesis, and edited in adult tissue, remains a matter of intense debate. Profiling of DNA methylation in many cell type, species and environmental set up indicates that the DNA methylation profile is thighly correlated with the cell type and its environment. As a consequence, de novo methylation and DNA demethylation events are not randomly distributed but are actually targeted to particular regulatory DNA elements in the genome, including promoters, enhancers or repeated DNAs. For this latter reason researchers have focused on the role of transcription factor in these DNA methylation events. Yet, it is also recognized that non-coding RNAs, short and long, contribute to the establishment and editing of DNA methylation profiles in mammals. Non-coding RNAs may directly interact and control methylation and demethylation activities and, as a consequence, the pattern of DNA methylation in the genome (Di Ruscio et al., 2013; Arab et al., 2014; Castro-Diaz et al., 2014; Molaro et al., 2014; Turelli et al., 2014). For instance, antisense long non-coding RNA TARID (TCF21 antisense RNA inducing demethylation), activates TCF21 expression by inducing promoter demethylation. TARID sequence is complementary to the sequence of the TCF21 promoter. Its transcription causes the anchoring of GADD45A (growth arrest and DNA-damageinducible, alpha), a regulator of DNA demethylation, at the TCF21 promoter and its subsequent chromatin remodelling (Arab et al., 2014). Understanding the interplay between noncoding RNAs and transcription factors in the establishment and the maintenance of DNA methylation is therefore an important challenge for the future.

The Expanding Role of MBD Genes in Autism: Identification of a MECP2 Duplication and Novel Alterations in MBD5, MBD6, and SETDB1

The methyl-CpG-binding domain (MBD) gene family was first linked to autism over a decade ago when Rett syndrome, which falls under the umbrella of autism spectrum disorders (ASDs), was revealed to be predominantly caused by MECP2mutations. Since that time, MECP2 alterations have been recognized in idiopathic ASD patients by us and others. Individuals with deletions across the MBD5 gene also present with ASDs, impaired speech, intellectual difficulties, repetitive behaviors, and epilepsy. These findings suggest that further investigations of the MBD gene family may reveal additional associations related to autism. We now describe the first study evaluating individuals with ASD for rare variants in four autosomal MBD family members, MBD5, MBD6, SETDB1, and SETDB2, and expand our initial screening in the MECP2 gene. Each gene was sequenced over all coding exons and evaluated for copy number variations in 287 patients with ASD and an equal number of ethnically matched control individuals. We identified 186 alterations through sequencing, approximately half of which were novel (96 variants, 51.6%). We identified seventeen ASD specific, nonsynonymous variants, four of which were concordant in multiplex families: MBD5 Tyr1269Cys, MBD6 Arg883Trp, MECP2 Thr240Ser, and SETDB1 Pro1067del. Furthermore, a complex duplication spanning the MECP2 gene was identified in two brothers who presented with developmental delay and intellectual disability. From our studies, we provide the first examples of autistic patients carrying potentially detrimental alterations in MBD6 and SETDB1, thereby demonstrating that the MBD gene family potentially plays a significant role in rare and private genetic causes of autism.

There is growing evidence of the involvement of the methyl-CpG-binding domain (MBD) genes in neurological disorders. To date, pathogenic mutations have been found in patients with clinical features along the autism continuum for two genes in this family, methyl-CpG-binding domain protein 5 (MBD5) and methyl-CpG-binding protein 2 (MECP2). Both genes carry an MBD domain, the unifying feature for the family that includes nine additional genes; BAZ1A, BAZ1B, MBD1,MBD2, MBD3, MBD4, MBD6, SETDB1 and SETDB2 (Roloff et al., 2003). The MBD genes are involved in a variety of functions, including chromatin remodeling (BAZ1A, BAZ1B, MBD1, MBD2, MBD3, and MECP2), DNA damage repair (BAZ1A and MBD4), histone methylation (SETBD1 and SETDB2), and X chromosome inactivation (MBD2, Roloff et al., 2003, Bogdanovic & Veenstra, 2009). There is also functional interplay among members of this family as they have been found to bind at the same promoter regions (MBD1, MBD2, MBD3, andMECP2), partner with each other in complexes (MBD1 and SETBD1), or act in the same complexes in a mutually exclusive manner (MBD2 and MBD3, Sarraf & I. Stancheva 2004; Ballestar et al., 2005; Le Guezennec et al., 2006; Matarazzo et al., 2007). Little is known thus far about the functions of MBD5 and MBD6; they each encode proteins that localize to chromatin but fail to bind methylated DNA (Laget et al., 2010).

While there is clinical evidence of MECP2 and MBD5 playing a role in autism, only two studies to date have evaluated patients with ASD for mutations in additional MBD family members (Li et al., 2005; Cukier et al., 2010). Previous work in our laboratory analyzed the coding regions of MBD1, MBD2, MBD3, andMBD4 in over 200 individuals with ASD of African and European ancestry and identified multiple variants that altered the amino acid sequence, were unique to patients with autism, and concordant with disease in multiplex families (Cukier et al., 2010). In contrast, a study by Li and colleagues was restricted to a dataset of 65 Japanese autistic patients and reported only a single variation that might be related to autism (Li et al., 2005). We now expand our initial study of MECP2 to a larger dataset that includes male patients and perform the first study evaluating patients with ASD for alterations in four additional MBD family members: MBD5, MBD6, SETDB1 and SETDB2.

Sequencing across the five MBD genes in 287 patients with ASD and 288 ethnically matched control individuals identified a total of 186 unique variations (Table 1, Supplemental Tables 3–7). These variants included 177 single nucleotide polymorphisms (SNPs), five deletions and four insertions. Ninety (48.4%) of the variations have been previously reported in either the dbSNP 134 database (http://www.ncbi.nlm.nih.gov/projects/SNP/) or RettBASE (http://mecp2.chw.edu.au/), while the remaining 96 variants (51.6%) are novel. Fifty-six variations are predicted to alter the amino acid sequence. Fifty-three of the changes were found solely in patients with ASD and absent from controls. To determine variants most likely to contribute to ASD susceptibility, we prioritized changes that were either unique to affected individuals or that had an increased frequency in cases when compared to controls. The 17 most interesting variants were nonsynonymous and unique to our ASD population (Table 1). We utilized four distinct programs to characterize the variants; GERP (Cooper et al., 2005) and PhastCons (Siepel et al., 2005) to measure the level of amino acid conservation across species and PolyPhen (Adzhubei et al., 2010) and SIFT (Kumar et al., 2009) to predict which alterations might have the damaging consequences to protein function.

The mutational burden between cases and controls of African or European ancestry for each gene was not statistically significant by the chi-squared test (Supplemental Table 8). This was determined for the overall load of all variants as well as nonsynonymous alterations (Supplemental Table 9).

MBD5

Thirty-two changes were identified in MBD5, 18 of which have been previously reported (Supplemental Table 3). A distinct set of 11 alterations were nonsynonymous, four of which were only identified in patients with ASD (Val443Met, Ile1247Thr, Tyr1269Cys, and Arg1299Gln, Figure 1A–D, Table 1). Three of these four alterations (75%) are predicted to be damaging by SIFT, as compared to only two of seven nonsynonymous variants (28.6%) identified solely in control individuals (Supplemental Table 3). One alteration of high interest, MBD5 Tyr1269Cys, was inherited paternally in all three ASD children in multiplex family 7763 (Figure 1C). Two of the affected individuals (0001 and 0100) were intellectually impaired with measured IQ in the moderate to severe range (Full Scale IQ: 40 and 50, respectively), while the remaining brother with autism (0101) had borderline intellectual functioning (Full Scale IQ=78). Furthermore, all three siblings had a delay in language and displayed self-injurious behaviors. Two individuals presented with macrocephaly (0100 and 0101), and individual 0100 has a history of epilepsy (recurrent non-febrile seizures).

MBD6

A total of 44 alterations were detected in MBD6, two being single base pair insertions and the remainder of which were SNPs (Supplemental Table 4). Sixteen of the single nucleotide changes have been previously reported and 28 are novel. A subset of 17 alterations was identified only in individuals with ASD, seven of which are predicted to cause missense changes (Table 1, Figure 1E–K). While each of these changes was only identified in a single proband, three of the alterations have high PolyPhen and SIFT scores and are novel (Arg883Trp, Pro943Arg and Arg967Cys), suggesting a strong functional consequence. Furthermore, one of these alterations, Arg883Trp, was identified in multiplex family 7979 and passed maternally to both affected children (Figure 1I). Individual 0001 has a diagnosis of autism and is nonverbal with moderate intellectual disability. His sister (0100) has a diagnosis of Pervasive Developmental Disorder-Not Otherwise Specified and mild intellectual disability, displaying some phrase speech. Both siblings have a history self-injurious behavior. Their mother (1001), who also carries the alteration, was diagnosed with anxiety/panic disorders, depression, obsessive compulsive disorder, and has a history of epilepsy (adolescent onset seizures).

Along with novel variations of interest in MBD6, we found that two known SNPs occur at a higher frequency within our affected population compared to our control population. The first variation, rs61741508 (c.-2C>A), was recognized in sixteen patients with ASD and five controls and is located just upstream of the ATG start site in the Kozak consensus sequence. This variation also has high conservation scores (Supplemental Table 4). The second SNP, rs117084250 (c.2407-64C>T), falls within intron nine and was found in twelve individuals with ASD but only four controls. However, the conservation scores were relatively low, thereby making this a variant of lesser interest (Supplemental Table 4).

MECP2

Twenty-eight alterations were identified in MECP2 (Supplemental Table 5). Sixteen of these are currently in the dbSNP database and another one has been previously reported in RettBASE, leaving 11 novel variations. While none of the frequently recurring, classic Rett syndrome variations were identified in this study, there are two previously reported MeCP2 alterations of undetermined pathogenicity (Thr240Ser and Ala370Thr) that may cause clinical phenotypes. This first variation, MeCP2 Thr240Ser (rs61749738), was identified in two families of African ancestry (1072 and 17130) and absent in control individuals (Figure 2A,B). Further investigation into additional family members showed that the variation was inherited maternally in both cases and concordant with disease in multiplex family 1072. The second alteration, Ala370Thr (rs147017239), was also inherited maternally in a single proband of African ancestry (family18024, Figure 2C).

SETDB1

A total of 44 changes were found in SETDB1, comprised of 19 known and 25 novel alterations (Supplemental Table 6). Eight changes are predicted to be nonsynonymous, but only one of these, Pro1067del, was found solely in patients with ASD. This change is also the only ASD specific, nonsynonymous deletion identified in the entire study. The variant removes three nucleotides and predicts an in-frame deletion of a single amino acid. This deletion falls within the SET domain of the protein and was inherited maternally in both affected sons in family 17187 (Figure 3A).

Another novel variation of interest in SETDB1 that we identified in a high proportion of cases versus controls, Pro529Leu, was identified in five ASD families of European ancestry and only a single control (Figure 3B–F). This variant was inherited paternally in one family and maternally in the remaining four families. In family 37265, the variation was passed from the father, who has dyslexia, to both the female proband with autism (0001) who was diagnosed with developmental and language delays as well as her brother (0100) who presented with ADHD, anxiety/panic disorder, language delay and macrocephaly (Figure 3E). In two of the families with maternal inheritance (17663 and 37673), the mothers presented with anxiety/panic disorder. In family 17663, the mother also presented with a history of seizures, sleep disorder and self-reported depression, while the mother in family 37673 reported history of adolescent onset Anorexia Nervosa. The increased incidence of this alteration in cases versus controls, along with neuropsychiatric and neurodevelopmental disorders in parents carrying the alteration, suggests that this variation may confer a variety of clinical consequences.

SETDB2

Thirty-eight single base pair alterations were identified in the SETDB2gene, 21 of which have been previously reported and the remaining 17 are novel (Supplemental Table 7). Eight SNPs are predicted to alter amino acids and three of these were unique to affected individuals: Ile425Thr, Thr475Met and Pro536Arg (Table 1, Figure 3C,G,H). However, these alterations are not predicted to have a highly detrimental effect on the protein and occur within singleton families, making it difficult to determine whether they may play a pathogenic role in ASD.

Along with isolating additional variations in MBD5 and MECP2 that may contribute to neuropsychiatric disease, this study is the first to report prospective pathogenic variations in MBD6 and SETDB1. These include two novel, nonsynonymous alterations in MBD6 (Arg883Trp and Pro943Arg) and one more in SETDB1 (Pro1067del). Furthermore, the MBD6 Arg883Trp and SetDB1 Pro1067del variations each segregated with ASD in the multiplex families. Potential for SETDB1 to play a role in neurobehavioral phenotypes is supported by results from transgenic Setdb1 mice demonstrating a role in mood behaviors (Jiang et al., 2010a).

To date, MBD5 mutations have been identified in individuals presenting a range of clinical phenotypes including ASD, developmental delay, intellectual disability, epilepsy, repetitive movements, and language impairments (Vissers et al., 2003;Koolen et al., 2004; de Vries et al., 2005; Wagenstaller et al., 2007; Jaillard et al., 2008; van Bon et al., 2009; Williams et al., 2009; Chung et al., 2011; Talkowski et al., 2011; Noh & Graham Jr 2012). These results suggest a significant role for theMBD5 isoform 1, which presents with increased expression in the brain (Laget et al., 2010). It has been estimated that between microdeletions and point mutations of MBD5, this gene may play a contributing genetic role in up to 1% of individuals with ASD (Talkowski et al., 2011). Of the nonsynonymous alterations identified in this study, ASD specific changes were more likely to be predicted to be damaging as compared to those variations found in control individuals (Supplemental Table 3). MBD5 Tyr1269Cys is a strong potentially pathogenic change due to its co-segregation with ASD in a multiplex family of three affected children, high conservation of this amino acid across species and altered function in the luciferase transcriptional activation assay. While this alteration does not fall in a known protein domain, it is specific to isoform 1, the isoform predominately expressed in brain (Laget et al., 2010). It seems likely that most alterations inMBD5 related to disease will be rare and unique, as the one alteration previously reported to have an increased frequency in patients with ASD, Gly79Glu, was only identified in a single control in the current study (Talkowski et al., 2011).

The role of MECP2 in developmental disorders is undisputed (Samaco & Neul 2011). Our study supports the possible pathogenicity of two specific MeCP2 alterations: Thr240Ser and Ala370Thr. The first variant, Thr240Ser was identified in two male probands from families of African ancestry, including the multiplex family 1072 where the variant segregated with ASD (Figure 2A,B). The maternal inheritance in family 17130 and presence of an unaffected carrier sister suggests that the variation may only present with a clinical phenotype in a hemizygous state. This variant falls within the transcriptional repression domain and has been previously reported in four studies; three cases of males with intellectual disability and one female with Rett syndrome (Yntema et al., 2002; Bourdon et al., 2003;Bienvenu & J. Chelly 2006; Campos et al., 2007; Bunyan & D. O. Robinson 2008). The second alteration, Ala370Thr, was identified in a singleton family of African ancestry and previously reported in three Chinese individuals: one female with Rett syndrome, her unaffected mother and a male presenting with epileptic encephalopathy (Figure 2C, Li et al., 2007; Wong & Li 2007). Both of these alterations must be further evaluated to isolate their potential functional consequences.

Finally, while we did identify variants of interest in four of the genes studied,SETDB2 alterations did not appear to be related to the occurrence of ASDs.

This is the first study to evaluate the coding regions of MBD5, MBD6, SETDB1, and SETDB2 for rare alterations in individuals with ASD. We identified novel point mutations predicted to be damaging and concordant with disease in multiplex families, as well as a complex duplication encompassing MECP2. Additional studies, ideally both in patients and animal models, are required to determine the precise consequences of these alterations. The results described here compound the evidence of MECP2 and MBD5’s involvement in ASDs and neurodevelopmental disorders and provide the first examples of autistic patients carrying potentially detrimental alterations in MBD6 and SETDB1. This study demonstrates the expanding role MBD genes play in autism etiology.

The Akt serine/threonine kinase (also called protein kinase B) has emerged as a critical signaling molecule within eukaryotic cells. Significant progress has been made in clarifying its regulation by upstream kinases and identifying downstream mechanisms that mediate its effects in cells and contribute to signaling specificity. Here, we provide an overview of present advances in the field regarding the function of Akt in physiological and pathological cell function within a more generalized framework of Akt signal transduction. An emphasis is placed on the involvement of Akt in human diseases ranging from cancer to metabolic dysfunction and mental disease.

The molecular mechanisms of Akt regulation are summarized in Figure 1.

Canonical schematic depicting the present state of our understanding of Akt activation and regulation of downstream biological responses. Autophosphorylation of RTKs induces the recruitment of p85 regulatory subunits leading to PI3K activation. Once activated, p110 catalytic subunits phosphorylate plasma membrane-bound phosphoinositides (PI-4-P and PI-4,5-P2) on the D3-position of their inositol rings. The second messengers resulting from this PI3K-dependent reaction are PI-3,4-P2and PI-3,4,5-P3 (also called PIP3). PIP3, in turn, is the substrate for the phosphoinositide 3-phosphatase PTEN, an endogenous inhibitor of PI3K signaling in cells. The phosphoinositide products of PI3K form high-affinity binding sites for the PH domains of intracellular molecules. PDK1 and Akt are two of the many targets of PI3K products in cells. Following binding of the Akt PH domain to PI3K products, Akt is phosphorylated by PDK1 on a critical threonine residue in its kinase domain. mTORC2 is the main kinase activity that through phosphorylation of a C-terminal HM serine residue locks Akt enzyme into an active conformation. Other kinases such as DNA-PK and ILK1 are also capable of phosphorylating Akt at the HM site but may do so in a cell- or context-dependent manner. Akt activation is blunted by phosphatases including PP2A and PHLPP that inhibit Akt activity by dephosphorylation. Studies examining Akt-interacting proteins such as CTMP or second messengers such as Ins(1,3,4,5)P4 suggest that this common pathway of Akt regulation may be further specified within certain functional contexts or during development. Once activated, Akt activation is channeled into a plethora of downstream biological responses reaching from angiogenesis, cell survival, proliferation, translation to metabolism.

Insights into the molecular consequences of increased Akt activation were derived from seminal studies that ultimately identified the ‘orphan’ proto-oncogene as an obligate intermediate downstream of PI3K in the insulin-dependent metabolic control of glycogen synthesis. When searching for kinases that could regulate GSK3, the groups of Brian Hemmings and Phil Cohen realized that Akt inhibited GSK3 activity in an insulin-stimulated and PI3K-dependent manner by direct phosphorylation of an N-terminal regulatory serine residue (Cross et al., 1995). By systematically permutating the amino-acid sequence surrounding the Akt phosphorylation site in GSK3, Alessi et al. (1996b) derived an optimal peptide sequence for Akt phosphorylation (R-X-R-X-X-S/T; where R is an arginine residue, S is serine, T is threonine and X is any amino acid). This Akt consensus motif is a common feature of known substrates of Akt, and its presence predicts reasonably well whether a given protein may be phosphorylated by Akt enzyme in vitro (for review, see Manning and Cantley, 2007). Experiments using randomized permutations on the basis of the motif to optimize substrate peptides have defined the requirement for optimal phosphorylation by Akt further (Obata et al., 2000). The preferred phosphoacceptor for Akt-dependent phosphorylation is a serine residue, but a synthetic substrate peptide with a threonine residue as the phosphoacceptor instead (R-P-R-A-A-T-F; P=proline, A=alanine, F=phenylalanine) is also easily phosphorylated. For achieving optimal phosphorylation efficiency, the phosphoacceptor is best followed by a hydrophobic residue with a large side-chain in the p+1 position, and preceded by a serine or threonine at the p−2 position.

One of the first targets of Akt to be identified that has direct implications for regulating cell survival is the pro-apoptotic BCL2-antagonist of death (BAD) protein. BAD regulation by Akt has exemplified the molecular pathways linking survival factor signaling to apoptosis suppression (for review, see Franke and Cantley, 1997). When BAD is not phosphorylated, it will inhibit Bcl-xL and other anti-apoptotic Bcl-2 family members by direct binding of its Bcl-2 homology domain to their hydrophobic grooves (Gajewski and Thompson, 1996). Once phosphorylated, these phospho-serine residues of BAD form high-affinity binding sites for cytoplasmic 14-3-3 molecules. As a result, phosphorylated BAD is retained in the cytosol where its pro-apoptotic activity is effectively neutralized (Zha et al., 1996). The importance of BAD as an integration point of survival signaling is underscored by the fact that it is a substrate for multiple independent kinase pathways in cells, not all of which phosphorylate BAD at the same site(s) as Akt (Datta et al., 2000). The mechanisms of 14-3-3-dependent regulation of BAD function hereby resemble the Akt-dependent inhibition of FoxO transcription factors that regulate the transcription of pro-apoptotic genes (Brunet et al., 1999).

The function of Akt extends beyond maintaining mitochondrial integrity to keep cytochrome c and other apoptogenic factors in the mitochondria (Kennedy et al., 1999). Akt activity also mitigates the response of cells to the release of cytochrome c into the cytoplasm. Although caspase-9 is an Akt substrate in human cells, where it may explain cytochrome c resistance (Cardone et al., 1998), it may not be the only, or even the most important, target because Akt-dependent cytochrome c resistance can be observed in animal species where caspase-9 lacks a potential Akt phosphorylation site (Fujita et al., 1999; Zhou et al., 2000). Not surprisingly, other components of the post-mitochondrial machinery such as the X-linked inhibitor of apoptotic proteins (XIAP) have been suggested as potential Akt substrates (Dan et al., 2004).

Another important class of Akt targets are proteins involved in the stress-activated/mitogen activated protein kinase (SAPK/MAPK) cascades. Growing experimental evidence points to a close functional relationship between the Akt survival pathway and SAPK/MAPK cascades that are activated by various cellular stresses and are linked to apoptosis. Increased Akt activity has been shown to suppress the JNK and p38 pathways (Berra et al., 1998; Cerezoet al., 1998; Okubo et al., 1998). It has been shown that apoptosis signal-regulating kinase 1 (ASK1) is regulated by Akt and contains an Akt-specific phosphorylation site (Kim et al., 2001). These findings have been confirmed independently by other groups (Yuan et al., 2003; Mabuchi et al., 2004). Thus, ASK1 is likely to be one of the points of convergence between PI3K/Akt signaling and stress-activated kinase cascades, although probably not the only one. Akt also phosphorylates the small G protein Rac1 (Kwon et al., 2000), the MAP2K stress-activated protein kinase kinase-1 (SEK1; also known as JNKK1 or MKK4) (Park et al., 2002) and the MAP3K mixed lineage kinase 3 (MLK3) (Barthwal et al., 2003; Figueroa et al., 2003). Using yeast-2-hybrid screens to identify interacting partners for Akt kinases, Figueroa et al. (2003) found binding of Akt2 to the JNK adaptor POSH. These authors showed that the binding of Akt2 to POSH results in an inhibition of JNK activity, and that this inhibition is mediated by phosphorylation of the upstream kinase MLK3 and leads to the disassembly of the JNK signaling complex. In turn, POSH is also an Akt substrate (Lyons et al., 2007). Taken together, these findings point to an intriguing model for the regulation of the JNK pathway by Akt, in which the Akt-dependent phosphorylation of specific components can block signal transduction through the stress-regulated kinase cascade. In spite of this, it has been reported that Akt also blocks the pro-apoptotic activity of other MAP3Ks such as MLK1 and MLK2 that act in parallel to MLK3 but do not contain a typical Akt consensus phosphorylation motif (Xu et al., 2001). Thus, phosphorylation-based mechanisms may be limited in explaining the role of Akt in blocking JNK signaling.

Although many of its substrates are involved in clearly defined biological functions within a circumscribed context such as cell proliferation, a more thorough analysis of Akt signaling has suggested that the boundaries between metabolic processes and apoptosis suppression may be artificial. For example, the Akt target GSK3 has been implicated both in the regulation of glucose metabolism and cell survival (Pap and Cooper, 1998). These findings suggest that the distinctions between cell growth, survival, metabolism and apoptosis regulation do not properly reflect functional interactions between concurrent biological processes in cells. This shift in perception has been fueled by studies from the Korsmeyer laboratory that have demonstrated a canonical function for the pro-apoptotic Bcl-2 family member BAD in the regulation of glucokinase activity (Danial et al., 2003). It is conceivable that findings of PKA-dependent regulation of BAD in glucose metabolism can be extrapolated to BAD inhibition by Akt. Still, a formal confirmation for a role of Akt in this process has yet to be presented (for review, see Downward, 2003).

Other studies suggest that the involvement of Akt in brain function extends beyond the protection of neuronal cells against apoptotic insults. Indeed, pathological changes in Akt signal transduction have been described that are associated with mental diseases. Significantly decreased Akt1 expression has been reported in patients suffering from familial schizophrenia (Emamian et al., 2004). Decreased Akt1 levels are correlated with increased GSK3 activity, presumably because of the lack of the Akt-dependent inhibitory input on GSK3. In support of AKT1 being a susceptibility gene for schizophrenia, Akt1(−/−) mice exhibit increased sensitivity to the sensorimotor disruptive effect of amphetamine, which is partly reversed by the treatment of mutant mice with the antipsychotic drug haloperidol (Emamian et al., 2004). Additional support for a contribution of impaired Akt signaling in the pathogenesis of schizophrenia derives from the finding of mutant PI3K signaling in schizophrenia (for review, see Arnold et al., 2005). A direct involvement of Akt in dopaminergic action is indicated by the observation that Akt1(−/−) mutant mice exhibit a behavioral phenotype resembling enhanced dopaminergic transmitter function (Emamian et al., 2004). By interacting with the GSK3 pathway, Akt modulates the suppression of dopamine (DA)-associated behaviors after treatment with the mood stabilizer lithium (Beaulieu et al., 2004). Furthermore, a β-arrestin 2-mediated kinase/phosphatase scaffold of Akt and protein phosphatase A (PP2A) is required for the regulation of Akt downstream of DA receptors (Beaulieu et al., 2005). Still, the role of Akt in dopaminergic responses by far exceeds actions downstream of DA receptors: the insulin-dependent regulation of DA transporter also depends on Akt activity (Garcia et al., 2005).

Since the field of Akt signaling in psychiatric disorders is still emerging, it may be too early to speculate about the molecular involvement of Akt in regulating higher brain function. Possible functional outlets of Akt include some of the substrates mentioned above, including mTORC1 and GSK3. In addition, substrates of Akt related to synaptic plasticity and transmission have been described. One such novel substrate of Akt related to neuronal excitability is the β2-subunit of the type A γ-aminobutyric acid receptor (GABAA-R) (Lin et al., 2001). In support of a direct involvement of Akt in synaptic function, studies directed at working memory performance performed in Akt1(−/−) mice (Lai et al., 2006) and in healthy individuals carrying the AKT1 coding variation observed in familial schizophrenia (Tan et al., 2008) find a strong correlation with cognitive performance. Additional roles for Akt in higher brain function are suggested by studies from the Nestler laboratory that have explored the IRS2-Akt pathway during the development of tolerance to opiate reward (Russo et al., 2007). By using viral-mediated gene transfer to express mutant Akt in midbrain neurons, these authors demonstrate that downregulation of the IRS2-Akt pathway mediates morphine-induced decreases in cell size of DA neurons in brain regions that are critically involved in the reward circuitry and affected in individuals addicted to drugs of abuse.

Finally, TSC patients show an increased incidence of autism spectrum disorders (ASD) ranging from 25 to 50% (for review, seeWiznitzer, 2004). Individuals with macrocephaly due to Lhermitte–Duclos disease are prone to ASD and show a pronounced incidence of mutations in the PTEN tumor suppressor gene (Butler et al., 2005). Additional experimental support for a possible involvement of PTEN/Akt in ASD is provided by data from the Parada laboratory examining the morphology and behavior of mutant mice with neuron-specific knockout of PTEN (Kwon et al., 2006). Future studies will be required to clarify the function of Akt in cognition and characterize the underlying molecular mechanisms. In spite of this, these initial studies suggest a complex function of Akt in conditions affecting brain function and mental health.

The emerging involvement of Akt in higher brain function is summarized in Figure 2.

When considering the present understanding of all the signals leading to and from Akt, we face a growing complexity that is in part compounded by the intersection of multiple signaling cascades. Many substrates of Akt are shared with other kinases that have similar specificities. Moreover, signals originating from activated Akt do not simply lead to changes in the biological activity of specific downstream substrates, but affect entire signaling networks. In spite of this, there is hope that there is order to the far-reaching physiological involvement of Akt. One possibility is that differential regulation of the binding partners of Akt may determine cell- and context-specific signaling by Akt. Studies are now needed to elucidate the physiological functions of the binding partners of Akt in mammalian physiology.

A second challenge that the field is facing arises from the involvement of Akt in multiple areas of physiology. These now exceed cancer and diabetes and, as briefly outlined above, include higher brain functions related to cognition.

SETDB1 in Early Embryos and Embryonic Stem Cells

Yong-Kook Kang

The histone methyltransferase SETDB1 contributes to the silencing of local chromatin and the target specificity appears to be determined through various proteins that SETDB1 interacts with. This fundamental function endows SETDB1 with specialized roles in embryonic cells. Keeping the genomic and transcriptomic integrity via proviral silencing and maintaining the pluripotency by repressing the differentiation-associated genes have been demonstrated as the roles of SETDB1 in embryonic stem cells. In early developing embryos, SETDB1 exhibits characteristic nuclear mobilizations that might account for its pleiotropic roles in these rapidly changing cells as well. Early lethality of SETDB1-null embryos, along with other immunolocalization findings, suggests that SETDB1 is necessary for reprogramming and preparing the genomes of zygotes and pluripotent cells for the post-implantation developmental program.

Exome sequencing of probands with autism have revealed broadly similar results:de novo mutations in a large set of genes occur in a significant fraction of patients, with relatively high OR’s for damaging mutations in genes expressed in the brain9,19–21. Most interestingly, CHD8, which like CHD7 reads H3K4me marks, is frequently mutated in autism22, raising the question of whether the H3K4me pathway may play a role in many congenital diseases. Among 249 protein-alteringde novo mutations in CHD (Supplementary Table 4) and 570 such mutations in autism9,19,20,23, there were two genes, CUL3 and NCKAP1, with damaging mutations in both CHD and autism and none in controls (P = 0.001, Monte Carlo simulation), and several others with mutations in both (e.g., SUV40H1 and CHD7). Similarly, rare copy number variants at 22q11.2, 1q21, and 16p11 are found in patients with autism, CHD or both diseases24–26. These observations suggest variable expressivity of mutations in key developmental genes. Identification of the complete set of these developmental genes and the full spectrum of the resulting phenotypes will likely be important for patient care and genetic counseling.

Since their discovery, microRNAs (miRNA) have been implicated in a vast array of biological processes in animals, from fundamental developmental functions including cellular proliferation and differentiation, to more complex and specialized roles such as longterm potentiation and synapse-specific modifications in neurons. This review recounts the history behind this paradigm shift, which has seen small non-coding RNA molecules coming to the forefront of molecular biology, and introduces their role in establishing developmental complexity in animals. The fundamental mechanisms of miRNA biogenesis and function are then considered, leading into a discussion of recent discoveries transforming our understanding of how these molecules regulate gene network behaviour throughout developmental and pathophysiological processes. The emerging complexity of this mechanism is also examined with respect to the influence of cellular context on miRNA function. This discussion highlights the absolute imperative for experimental designs to appreciate the significance of context-specific factors when determining what genes are regulated by a particular miRNA. Moreover, by establishing the timing, location, and mechanism of these regulatory events, we may ultimately understand the true biological function of a specific miRNA in a given cellular environment.

It was once considered the central dogma of molecular biology that gene expression was regulated in a unidirectional manner whereby cellular instructions were encoded in DNA to be transcribed to produce RNA, which simply acted as a messenger molecule to produce the protein end-products that executed these cellular instructions. In fact, signs of a biological phenomenon whereby non-protein-coding RNA molecules could interfere with this very process were not even realized until the 1970s and early 1980s, when exogenous oligonucleotides complementary to ribosomal RNA were found to interfere with ribosome function (Taniguchi and Weissmann, 1978; Eckhardt and Luhrmann, 1979;Jayaraman et al., 1981). A number of experiments in both prokaryotes and eukaryotes further supported the notion of antisense RNA as an antagonist to RNA function (Chang and Stoltzfus, 1985; Ellison et al., 1985; Harland and Weintraub, 1985; Izant and Weintraub, 1985; Melton, 1985), and one such experiment elegantly demonstrated that the introduction of synthetic oligonucleotides complementary to 3′- and 5′-terminal repeats of Rous sarcoma virus 35S RNA not only attenuated viral replication and cell transformation, but also inhibited viral RNA translation in vitro (Stephenson and Zamecnik, 1978; Zamecnik and Stephenson, 1978).

In addition to this, the successful inhibition of thymidine kinase gene expression by antisense RNA in eukaryotic cells precipitated the concept of antisense RNA not only as an experimental tool, but also as a therapeutic design (Izant and Weintraub, 1984). Determining the functionality of a previously identified gene sequence without identifying, isolating, or characterizing the protein product; interfering with RNAs that are never translated; and silencing the expression of disease-associated transcripts in a sequence-specific manner: these were some very appealing prospects. By the late 1980s and early 1990s, a variety of techniques had evolved in the field of molecular and applied genetics whereby various antisense DNA and RNA construct designs were employed to efficiently downregulate target gene expression (Fire et al., 1991).

It was only a matter of time before phenomena of gene silencing began to unfold in animals. Previous work in the 1980s with Caenorhabditis elegans had established that mutations in the genes for lin-4, lin-14, lin-28, lin-29, and lin-41 altered the heterochronic lineage of developing larvae, resulting in a failure to control temporal aspects of post-embryonic development (Chalfie et al., 1981; Ambros and Horvitz, 1984; 1987; Ambros, 1989); thus, these genes were referred to as being ‘heterochronic’. However, in 1993 it was discovered that lin-4 was located within an intron and was thus unlikely to encode a protein. More significantly, two lin-4 transcripts ∼22 and 61 nucleotides in length were identified that exhibited complementarity to a repeat sequence element in the 3′ untranslated region (UTR) of lin-14 mRNA (Lee et al., 1993). With another report soon replicating this finding in C. elegans andCaenorhabditis briggsae (Wightman et al., 1993), the notion was set forth that the 22-nucloetide lin-4 transcript represented an active mature form of the 61-nucelotide transcript and functioned to control worm larval development by binding to the 3′-UTR of lin-14, thereby negatively regulating its function via an antisense RNA–RNA interaction. Furthermore, lin-4 exhibited complementarity to seven regions within the 3′-UTR of lin-14, demonstrating that gene expression was more potently inhibited as more of these non-coding transcripts bound to the mRNA (He and Hannon, 2004). Retrospectively, we can identify the lin-4 gene in C. elegans as the pioneer of a new class of small, non-coding RNAs called microRNA (miRNA) (Lee et al., 1993), which utilize the RNA interference (RNAi) pathway to regulate the expression of protein-encoding genes at post-transcriptional level (He and Hannon, 2004).

The following few years were somewhat quiet at the forefront of miRNA research, with lin-4 mechanism assumed to be a unique event. Meanwhile, RNAi was coming to prominence in 1998 with Fire and Mello (along with their colleagues) reporting double-stranded RNA (dsRNA) to be far more potent at mediating gene suppression in C. elegans than single-stranded antisense RNA (Fire et al., 1998). Interestingly, only small quantities of dsRNA were required to induce post-transcriptional gene silencing (PTGS), and it was hypothesized that an endogenous catalytic or amplification component was mediating mRNA degradation prior to translation (Montgomery et al., 1998). RNAi was soon thereafter reported as an ATP-dependent process in an in vitroDrosophila embryo lysate system where dsRNA was processed into 21–23-nucleotide species that appeared to guide sequence-specific mRNA cleavage (Zamore et al., 2000). When dsRNA was shown by the Tuschl laboratory to be processed into 21–22-nucleotide short interfering RNA (siRNA) by a ribonuclease III enzyme to mediate sequence-specific RNAi in human embryonic kidney HEK-293 cells, the prospect was set forth for exogenous 21–22-nucleotide siRNA to be developed as gene-specific therapeutic molecules (Elbashir et al., 2001a).

With incredible excitement surrounding the implications of RNAi, Ruvkun and colleagues discovered a second miRNA inC. elegans in 2000. Like lin-4, the newly discovered let-7 exhibited complementarity to the 3′-UTR of heterochronic genes, in this case lin-14, lin-28, lin-41, lin-42, and daf-12 (Reinhart et al., 2000). Moreover, they discovered that let-7 was highly conserved in its temporal regulation across phylogeny (Pasquinelli et al., 2000), refuting the widely believed concept that lin-4 and let-7 were a worm-specific oddity and propelling miRNA to significance as native endogenous clients of the RNAi machinery. This catalysed intense genome-wide searches for the discovery of more endogenous small regulatory RNAs in numerous species, to the point that miRBase Release 19 currently contains sequence data for 25141 mature miRNA products in 193 organism species (Kozomara and Griffiths-Jones, 2011).

The significance of non-coding RNA was further illuminated in 2001 when the completion of the human genome project revealed that <2% of the human genome encoded proteins (Lander et al., 2001). It has been realized that the ratio of non-coding to protein-coding DNA in the genome correlates with developmental complexity (Mattick, 2004), and a recent publication has reported on the exponential correlation of miRNA gene number and 3′-UTR length—but not 5′-UTR or coding sequence length—with morphological complexity in animals (Chen et al., 2012). This was measured according to the number of cell types within each organism, and also confirmed earlier observations that 3′-UTR length in housekeeping genes has remained short across organisms, thereby minimizing miRNA-binding site potential and reducing the complexity with which these constitutively expressed genes are regulated (Stark et al., 2005). Today we certainly have a stronger appreciation for RNA molecules to function not only as messengers of protein production, but also as complex regulatory molecules facilitating the intricate control of gene expression required for developmental complexity (Kosik, 2009).

Mechanisms of miRNA function

When considering non-coding RNA function, miRNAs constitute one of the largest classes of endogenous, non-coding regulatory RNA molecules in animals. In their mature form they are ∼19–22 nucleotides in length, and they interact via Watson–Crick binding with regions of complementarity primarily within the 3′-UTR of mRNA transcripts. In doing so, miRNAs act as sequence-specificity guides for the RNAi machinery to mediate repression of target gene expression at post-transcriptional level by negatively regulating mRNA stability and/or protein translation.

miRNA biogenesis

miRNAs are typically transcribed by RNA polymerase II (pol II) as long primary miRNA (pri-miRNA) transcripts, which undergo sequential cleavage into a precursor miRNA (pre-miRNA) transcript before being cleaved again into the mature miRNA duplex (Figure 1). These pri-miRNA transcripts range in length from several hundred nucleotides to several kilobases, can contain either a single miRNA or clusters of several miRNAs, and originate from intronic regions of protein-coding and non-coding genes, as well as from intergenic and exonic regions (Rodriguez et al., 2004; Saini et al., 2007). The microprocessor complex is responsible for mediating pri-miRNA cleavage, with the dsRNA-binding protein DGCR8 (DiGeorge syndrome critical region gene 8) binding the pri-miRNA and positioning the catalytic site of Drosha—a ribonuclease III (RNase III) dsRNA-specific endonuclease—11 nucleotides from the base of the duplex stem to mediate nuclear processing to the pre-miRNA transcript (Denli et al., 2004; Han et al., 2006). This produces a pre-miRNA hairpin typically 55–70 nucleotides in length with a two-nucleotide 3′ overhang, characteristic of RNase III-mediated cleavage (Lee et al., 2003). This two-nucleotide overhang facilitates the subsequent exportation of the pre-miRNA from the nucleus to the cytoplasm by a RanGTP/Exportin5-dependent mechanism and is suspected to also facilitate subsequent cleavage by the RNase III endonuclease Dicer (Yi et al., 2003; Bohnsack et al., 2004; Lund et al., 2004). This cleavage requires the interaction of Dicer with the dsRNA-binding protein TRBP [HIV-1 transactivating response (TAR) RNA-binding protein] (Forstemann et al., 2005), and as a result of Dicer processing the terminal base pairs and the loop of the pre-miRNA are excised. This produces a 19–22-nucleotide mature miRNA duplex, which possess two-nucleotide overhangs at each 3′ end (Lee et al., 2002).

A model for canonical miRNA biogenesis and function in animals. After their transcription by RNA polymerase II, pri-miRNAs are cleaved in the nucleus by Drosha, which forms a microprocessor complex with DGCR8. This generates the pre-miRNA, which is actively exported into the cytoplasm via a RanGTP/Exportin 5-dependent mechanism. In the cytoplasm, Dicer binds the base of the pre-miRNA stem defined in the nucleus by Drosha. Dicer cleavage liberates a mature miRNA duplex that exhibits imperfect complementarity. This miRNA duplex is assembled into the miRISC loading complex, in which the passenger strand is discarded. The miRNA guides the mature miRISC to regions of complementarity within mRNA transcripts, thereby mediating post-transcriptional gene silencing through translational repression and/or mRNA degradation.

miRISC loading

After their maturation into small RNA duplexes, miRNAs are loaded into ribonucleoprotein (RNP) complexes, often referred to as miRNA-induced silencing complexes (miRISCs), RISCs, or miRNPs. The signature components of each miRISC are the miRNA and an Argonaute (AGO) protein. In humans, there are four AGO proteins (AGO1-4), each consisting of the highly conserved P-element-induced wimpy testes (PIWI), middle (MID), and PIWI-AGO-Zwille (PAZ) domains, along with a less-conserved terminal domain. The loading of the miRNA into this protein complex has been proposed to occur in tandem with Dicer-mediated miRNA maturation (Gregory et al., 2005;Maniataki and Mourelatos, 2005) and requires ATP hydrolysis with additional chaperone proteins to create an open conformation to facilitate loading of the miRNA duplex (Liu et al., 2004; Yoda et al., 2010).

A key feature of miRNA is that while both strands of a small RNA duplex are capable of activating the miRISC, typically only one strand will induce silencing (Khvorova et al., 2003). This asymmetry is primarily governed by the relative thermodynamic properties of the RNA duplex, such that the miRISC-associated helicase preferentially unwinds the miRNA duplex from the end with least resistance in terms of inter-strand hydrogen bonding. The strand with its 5′ end at this less thermodynamically stable end is selected as the guide strand, and proteins such as TRBP or protein kinase, interferon-inducible dsRNA-dependent activator (PACT) are proposed to interact with Dicer to sense this thermodynamic asymmetry (Schwarz et al., 2003; Noland et al., 2011). In doing so, the guide strand is retained in the miRISC, while the other strand (the passenger, or the miRNA* strand) is discarded (Hutvagner, 2005; Matranga et al., 2005). miRNA strand selection also appears to be independent of Dicer processing polarity (Preall et al., 2006), where both ends of a duplex have similar thermodynamic properties, both the miRNA and miRNA* act as the guide strand with similar frequencies (Schwarz et al., 2003). However, strand selection does not always occur according to the axiom of thermodynamic strand asymmetry, with tissue-specific factors appearing to play a role in enabling both the miRNA and miRNA* strands to co-accumulate and function as the guide strand (Ro et al., 2007). For this reason, miRNA nomenclature has advanced beyond the miRNA* system, with the adoption of miRNA-5p and -3p names to indicate whether the mature miRNA sequence is derived from the 5′ or 3′ end of the pre-miRNA transcript.

Once the mature miRNA strand has been isolated in the mature miRISC, the AGO protein functions as an interface for the miRNA to interact with its mRNA targets. Recent characterization of human AGO2 has revealed that the 3′ hydroxyl of the miRNA inserts into a hydrophobic pocket of AGO such that the terminal nucleotide stacks against the aromatic ring of a conserved phenylalanine residue in the AGO PAZ domain (Jinek and Doudna, 2009). Meanwhile, the MID domain forms a binding pocket that anchors the miRNA 5′ phosphate such that this terminal nucleotide is distorted and does not interact with the target mRNA (Ma et al., 2005; Parker et al., 2005).

…….

Since being discovered as regulators of developmental timing in C. elegans, it has become widely established that miRNA-mediated regulation of gene expression is a fundamental biological phenomenon required to facilitate key developmental processes such as cellular proliferation, programmed cell death, and cell lineage determination and differentiation (Bartel, 2009; Ambros, 2011). Their significance is such that 60% of the human genome is predicted to be regulated by miRNA function (Friedman et al., 2009), each miRNA estimated to regulate around 200 target genes (Krek et al., 2005).

miRNAs play a central role in establishing the spatiotemporal gene expression patterns required to establish specialized cell types and promote developmental complexity. The inherent complexity of miRNA function, however, requires a scientific approach in which context-specific miRNA function must be acknowledged if advancements are to be made in understanding how these small regulatory RNA molecules function in various developmental and pathophysiological processes. While this requires an appreciation for mechanistic aspects such as non-redundant miRISC function and the dynamic regulatory outcomes this facilitates, arguably the greatest challenge facing miRNA biology is the identification of the many genes that each miRNA targets and an understanding of the context-specific factors that determine when and how these genes are regulated.

It is natural to dream. Dreaming is a flashback recording of recent occurrences associated with cleaning out the memory of daily events. There is much written in both literature and in neuroscience as I write this. Dreaming is both natural and perhaps also adaptive, and dreams may be distressing or unintelligible in circumstances that we view as pathological. This is thought to be related to a loss of plasticity of the memory circuits. This occurs with mood disorders, sleep disorders with and without leg movement disorder, and with schizophrenia.

The term given by Martin Luther King, “I had a dream” is out of place under the cirumstances I describe. I am identical twin with a nonidentical triplet sister. Our brother died more than a decade ago, prematurely aged from living with schizophrenia. I and my sister could talk to him and understand what he said, even though it meant nothing to others. What I did not share was the total fragmentation of mental thoughts at an early age. Both I and my sister had guilt over the situation for years. I sought psychiatric assistance that went on for years. But I was not schizophrenic and I had a successful career by any standard, but was burdened trying to make up in achievement what was denied to my immigrant father and to my identical twin brother. It was by no means easy in the 1960s for my parents to deal with the situation, with a societal lack of understanding, a feeling of what have I done wrong, and a serious cost burden.

I went to medical school, which I had decided as a child, when I read Paul de Kruif’s Microbe Hunters.
I had a sister two years older who was a “wunder” kind, who I tried to follow. She had a GM scholarship and set the class curve as an undergraduate in a graduate course in numbers theory. Fortunately, my best friend, who was as brilliant as they come and a Merit Scholar college entry, cautioned not to overburden myself with the chemistry/math major that I never declared. My brother entered the hospital as I entered medical school, and the first year that would be expected to be difficult, certainly was for me.

Only in the last 5 years did I learn from extensive testing that I had a very high intelligence to match my achievement , but that I had Asperger’s. I also learned that I had an uncommon double mutation of the hydroxymethyl-folate reductase gene that is associated nonspecifically with neurological disorders. I take methyl folate for the genetic disorder to give access of folic acid to cross the blood brain barrier.
I’m retired for several years and had enormous difficulty in retiring, and was a workaholic. Work had great meaning and rewards for me.

I am now 74 and had a difficult 3 years with illness and hospitalization for me and my spouse of 45 years. We moved to be near my younger daughter, son-in-law, and grandson. This has brought great satisfaction. All the same, my asthma, sleep apnea, and general condition declined, and the move was more difficult than any I previously experienced. I have vivid dreams that requires clonipin for relief initially.

I have had increased frequency of dreams that can be resolved. However, with my awareness of the suicide of Robin Williams, I was given an awareness of his situation beyond what one would expect who has not seen such patients or has not experienced this. In my situation it was worsened by added depression. In the recent events I thought for the first time how incredible it was for my brother to have experienced this much of his schizophrenic life, even though I am not schizophrenic by any measure.

What’s in a dream?

I have had dreams before that I thought were interesting because of the people who I knew and the situations, that might have been unusual and gave me an inclination to write down. If I collected these, it could perhaps warrant a collection of stories. Those that are very recent have suggested that the one when I entertained my grandson is worthwhile. It was not so noxious, but it does fit the pieces together.
I watched some of the reporting of election returns of republican and democratic candidates. I sort of tossed around and played with the exceptional 6 year old who need not be exposed to such nonsence as we are seeing. It was early evening and to finish his limited allowable screentime, Nanny and Grandpa, and grandchild watched a children,s movie before bedtime. It was … … a takeoff on Red Ridinghood, with good cartoon figures, some recognizable voices, and an interesting storyline. Yes, LRRH does go through the woods to see her grandma, and she meets the wolf, who goes to her grandma’s house. Her grandma is tied up in the closet, and the woodsman, in the role of Paul Bunyan, gives a visit at the time of rescue. The storyline becomes a detective story to cull out the events leading up to a criminal event – who stole grandma’s recipe book, with a long family line of cooking. The grandma was an Olympic skiing champ who beets out the characters who stole her cookbooks. I’ll say no more than that the search comes upon grandma and LRRH escape with a parachute finish and the bad guys, led by a crafty rabbit, slide down on a ski-tram into a waiting police car. So that evening I have a dream that is a cockamaimie replay in which I am driving on the highway and enter a tunnel (like the rail in the movie), and the lane is cluttered with a wolf, and other creatures, making passage quite impossible.

I talked to my sister who called the next day. It was terrific when she said that if I had a pad and wrote them down immediately, they would form a pattern. Again, I have a dream, and I recall there was a pattern of feeling of failure. I am on Gabapentin for the restless leg. This time I have my brother (impossible) in it, I left my coat in a conference room and can’t get it immediately, and I have to return home with an exam the next day. In a recurring pattern, my brother is to drive. I can’t drive because of now having a diplopia from thyroid eye disease related to Grave’s disease. The exam has two questions about plasma from unclotted blood that is spun down and serum from clotted blood. This is very basic. The pattern is related to systemic notions of failure. My sister had a repeated pattern of rushing to get to the classes she teaches and not getting there on time (consistent with her rush rush).

I go back to bed and get another few hours of sleep. We had watched a number of Miss Marple movies recently. In the move I had the stressful experience of going through 40 years of save photographic equipment and photography, research literature, computer stuff, ya da, ya da, ya da. Very thorough, and tiring. The old lady in RRH and Miss Marple were merged into a character in a story related to the corroded pipes in Flint, and a criminal search for the cause of this problem (having watched the debate). Incredibly, this character was going through the material so rapidly, uncovering clues, and I was amazed.
I was struggling to keep up. Then I woke up. So my spouses assurances were correct. This is actually normal dreaming.

It is disturbing, consistent with a recent New York Times article on how the brain cleans out the garbage. I have too much garbage. My medication does have to be adjusted. It is perhaps not the same as my late brother’s experience. My sister’s observations have been helpful. My brother’s dreams were recognizable to me, but not to others, but they also had patterns, but patterns that were more distorted. If mine have been “normal”, but more frequent, this suggests a failure in the brain’s plasticity as I am aging, perhaps from from the stress in a major move. It is perhaps to be viewed as distressing at best compared with the worst case (my brother, or Robin Williams).

This is substantiated by my remembrance of driving on Woodward avenue or the expressway in Detroit, Michigan. I grew up on 2967 Sturtevant off of Dexter Ave. My elementary school no longer exists. We moved to the Northwest section and I graduated from Mumford High School in 1961. I lived in Trumbull, adjacent to Bridgeport, CT for 33 years, where my children grew up. The bizarreness of my recurring dream pattern has to do with a repeated driving and confusion between Detroit and Connecticut. I drove from Connecticut to New York for the last five years before retirement, but I failed to record these experiences. I had two car accidents related to narcolepsy in asbout 7 years related to my sleep apnea prior to getting it treated. In the last, I went to New Jersey to see an associate and driving back to Trumbull I veered off the highway and managed to veer into a tree in the snow. Fortunately I was able to control the car at the last minute. Fortunately, this could be much worse.